Relevant bibliographies by topics / Data Privacy in FinTech

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Data Privacy in FinTech'

Author: Grafiati
Published: 5 June 2025
Last updated: 16 July 2025

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Journal articles on the topic "Data Privacy in FinTech"

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Adedoyin Tolulope Oyewole, Bisola Beatrice Oguejiofor, Nkechi Emmanuella Eneh, Chidiogo Uzoamaka Akpuokwe, and Seun Solomon Bakare. "DATA PRIVACY LAWS AND THEIR IMPACT ON FINANCIAL TECHNOLOGY COMPANIES: A REVIEW." Computer Science & IT Research Journal 5, no. 3 (2024): 628–50. http://dx.doi.org/10.51594/csitrj.v5i3.911.

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In an era where the digital transformation of financial services is both a boon and a battleground, this paper meticulously navigates the intricate relationship between Financial Technology (FinTech) and the evolving landscape of data privacy laws. With the digital economy's expansion, FinTech companies stand at the forefront of innovation, offering unprecedented financial inclusion and efficiency opportunities. However, this rapid advancement also raises significant concerns regarding data privacy and consumer protection, necessitating a delicate balance between innovation and compliance. This study aims to dissect the complexities inherent in this relationship, exploring the impact of data privacy laws on FinTech, regulatory compliance challenges, and opportunities for fostering trust and innovation within the digital financial ecosystem.
 Employing a qualitative research design, the paper delves into a comprehensive review of scholarly literature, legal documents, and regulatory frameworks to illuminate the multifaceted dynamics at play. The findings reveal a nuanced "Innovation Trilemma," where FinTech's drive for innovation often collides with the imperative for market integrity and regulatory clarity. The study underscores the critical role of ethical considerations in FinTech adoption, highlighting the importance of integrating ethical practices to safeguard consumer rights and data protection.
 Conclusively, the paper advocates for regulatory adaptability, ethical innovation, and collaborative engagement among stakeholders as essential strategies for navigating the complexities of the digital financial landscape. It calls for a concerted effort to foster an ecosystem where innovation thrives alongside robust consumer protection and market integrity, paving the way for a sustainable, inclusive and ethically grounded FinTech future.
 Keywords: Financial Technology, Data Privacy Laws, Regulatory Compliance, Innovation Trilemma, Ethical FinTech, Digital Financial Ecosystem.
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Oluwaseyi, Olakunle Mokuolu. "Achieving data privacy and security in fintech cloud computing environments." World Journal of Advanced Research and Reviews 23, no. 3 (2024): 251–55. https://doi.org/10.5281/zenodo.14910402.

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The rapid adoption of Financial Technology (FinTech) has transformed the financial services industry, with cloud computing playing a crucial role in enabling scalable and efficient services. However, the reliance on cloud environments has also introduced significant data privacy and security challenges. This study explores strategies for achieving robust data privacy and security in FinTech cloud computing environments. Through a comprehensive literature review and qualitative analysis, this study identifies key threats, evaluates existing solutions, and proposes new approaches to safeguard data in FinTech. The findings suggest that a multi-layered security framework incorporating encryption, access control, and regulatory compliance is essential for mitigating risks. Future research should focus on developing standardized protocols and enhancing user awareness to address emerging challenges in this dynamic field.
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Tektona, Rahmadi Indra. "Legal Implications of Consumer Personal Data Misuse by OJK Licensed Fintech Lending Operators." Arena Hukum 17, no. 1 (2024): 43–63. http://dx.doi.org/10.21776/ub.arenahukum.2024.01701.3.

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The Financial Services Authority (OJK) has released a list of registered and licensed fintechs. The Investment Alert Task Force has taken firm action against illegal fintech lending that has the potential to break the law, along with the Indonesian National Police and the Ministry of Communication and Information. Privacy is violated when personal data are collected and shared. In the misuse of fintech lending consumer personal data, consumer rights are violated, and the loss is in the form of immaterial compensation. The legal implications are not only legal consequences with violations committed by the organizer, which result in the imposition of sanctions by the state. This normative legal research uses statutory, conceptual, and comparative approaches with a deductive-analytical method to explain the importance of regulating of personal data consumer protection on fintech lending operators.
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Bibhu, Dash. "Federated Learning for Privacy-Preserving: A Review of PII Data Analysis in Fintech." International Journal of Software Engineering & Applications (IJSEA) 13, no. 4 (2022): 1–13. https://doi.org/10.5281/zenodo.7061473.

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There has been tremendous growth in the field of AI and machine learning. The developments across these fields have resulted in a considerable increase in other FinTech fields. Cyber security has been described as an essential part of the developments associated with technology. Increased cyber security ensures that people remain protected, and that data remains safe. New methods have been integrated into developing AI that achieves cyber security. The data analysis capabilities of AI and its cyber security functions have ensured that privacy has increased significantly. The ethical concept associated with data privacy has also been advocated across most FinTech regulations.
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Oluwaseyi Olakunle Mokuolu. "Achieving data privacy and security in fintech cloud computing environments." World Journal of Advanced Research and Reviews 23, no. 3 (2024): 251–55. http://dx.doi.org/10.30574/wjarr.2024.23.3.2675.

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The rapid adoption of Financial Technology (FinTech) has transformed the financial services industry, with cloud computing playing a crucial role in enabling scalable and efficient services. However, the reliance on cloud environments has also introduced significant data privacy and security challenges. This study explores strategies for achieving robust data privacy and security in FinTech cloud computing environments. Through a comprehensive literature review and qualitative analysis, this study identifies key threats, evaluates existing solutions, and proposes new approaches to safeguard data in FinTech. The findings suggest that a multi-layered security framework incorporating encryption, access control, and regulatory compliance is essential for mitigating risks. Future research should focus on developing standardized protocols and enhancing user awareness to address emerging challenges in this dynamic field.
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Zakaria, Shahsuzan, Suhaily Maizan Abdul Manaf, and Mohd Talmizie Amron. "Fintech Frenzy: An engaging review of the transforming financial services." Environment-Behaviour Proceedings Journal 9, SI19 (2024): 103–8. http://dx.doi.org/10.21834/e-bpj.v9isi19.5775.

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Advancements, opportunities, and challenges in financial technology (fintech) are comprehensively reviewed in this paper. Fintech, powered by AI and blockchain, revolutionises access to financial services. The narrative review analyses academic databases and industry reports, highlighting fintech themes like mobile payments, peer-to-peer lending, robo-advisors, and blockchain. Opportunities include accessible services, efficiency, and cost savings. However, challenges like cybersecurity, regulation, financial inclusion, resistance to change, and data privacy must be addressed. Responsible and ethical fintech practices are crucial for security, regulation compliance, inclusion, and confidentiality. Fintech's potential to transform finance and enhance lives worldwide is emphasised.
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SINGH, NIDHI. "Data Security and Consumer Trust in Fintech Innovations using Technology Adoption Method." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 05 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem33015.

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This study investigates the interplay between data security and consumer trust in the context of fintech innovation, employing the Technology Adoption Model (TAM) as the theoretical framework. Fintech, characterized by its rapid technological advancements, has revolutionized traditional financial services, offering convenience and efficiency. However, concerns about data security and privacy have emerged as significant barriers to consumer adoption and trust. The research methodology involves a comprehensive literature review to establish the theoretical foundation and empirical analysis of consumer perceptions through surveys and interviews. The TAM framework, which examines users' attitudes and behaviors towards technology adoption, serves as a lens to understand how data security measures influence consumer trust in fintech platforms. Results indicate that consumers prioritize data security and privacy when evaluating fintech services, with perceptions of security significantly impacting trust levels. Factors such as encryption protocols, authentication mechanisms, and regulatory compliance play crucial roles in shaping consumer perceptions of data security. Additionally, transparency and communication regarding data handling practices enhance trust and mitigate concerns. The study identifies the role of user experience design in fostering trust, as intuitive interfaces and clear privacy policies contribute to perceived security. The findings underscore the importance of proactive measures by fintech companies to address data security concerns, including robust cybersecurity protocols, regulatory compliance, and transparent communication strategies. The study highlights the intricate relationship between data security and consumer trust in fintech innovation, emphasizing the need for comprehensive approaches to mitigate risks and build consumer confidence. By understanding and addressing consumer perceptions and concerns, fintech companies can enhance trust and drive widespread adoption of innovative financial technologies. Keywords: fintech, adoption, security, trust, inclusion, investment, payment, insurance, technology, efficiency, innovation.
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Bibhu, Dash. "FEDERATED LEARNING FOR PRIVACY-PRESERVING: A REVIEW OF PII DATA ANALYSIS IN FINTECH." International Journal of Software Engineering & Applications (IJSEA) 13, no. 4 (2023): 1–13. https://doi.org/10.5281/zenodo.8261226.

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There has been tremendous growth in the field of AI and machine learning. The developments across these fields have resulted in a considerable increase in other FinTech fields. Cyber security has been described as an essential part of the developments associated with technology. Increased cyber security ensures that people remain protected, and that data remains safe. New methods have been integrated into developing AI that achieves cyber security.
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Omolara Patricia Olaiya, Temitayo Oluwadamilola Adesoga, Azeez Adekunle Adebayo, Fehintola Moyosore Sotomi, Oluwaseun Aaron Adigun, and Paschal M Ezeliora. "Encryption techniques for financial data security in fintech applications." International Journal of Science and Research Archive 12, no. 1 (2024): 2942–49. http://dx.doi.org/10.30574/ijsra.2024.12.1.1210.

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In the dynamic world of financial technology (Fintech), securing financial data is a key priority. Increasing digital connectivity, adoption of cloud-based services requiring complex measures to protect the integrity, privacy and availability of sensitive information. Encryption techniques are emerging as a key tool to achieve these goals about itself by converting plaintext into ciphertext, protected from unauthorized access and probability violations. This review paper examines the various encryption techniques required to secure financial information in fintech applications. The main methods described include symmetric encryption, asymmetric and hybrid encryption techniques. Additionally, the function of end-to-end encryption (E2EE) is discussed in terms of protecting data privacy while it is being sent, which is essential for safeguarding sensitive financial activities such as mobile banking and digital payments. With its sophisticated method of permitting calculations on encrypted data without the need for decryption, homomorphic encryption shows promise for facilitating safe data analysis in Fintech settings while preserving data confidentiality. Each encryption method is scrutinized in terms of its strengths, weaknesses and practical applications in Fintech. Considerations such as computing efficiency, scalability, and regulatory compliance are addressed to provide insights for optimizing data protection strategies while adhering to industry standards and regulatory frameworks. The future of Fintech security is expected to be shaped by new developments in encryption technology, including post-quantum cryptography, artificial intelligence integration for adaptive security measures, and privacy-preserving solutions. The goal of these advancements is to strengthen the robustness of financial data security techniques in an increasingly linked digital world while mitigating changing cyber risks.
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Dash, Bibhu, Pawankumar Sharma, and Azad Ali. "Federated Learning for Privacy-Preserving: A Review of PII Data Analysis in Fintech." International Journal of Software Engineering & Applications 13, no. 4 (2022): 1–13. http://dx.doi.org/10.5121/ijsea.2022.13401.

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There has been tremendous growth in the field of AI and machine learning. The developments across these fields have resulted in a considerable increase in other FinTech fields. Cyber security has been described as an essential part of the developments associated with technology. Increased cyber security ensures that people remain protected, and that data remains safe. New methods have been integrated into developing AI that achieves cyber security. The data analysis capabilities of AI and its cyber security functions have ensured that privacy has increased significantly. The ethical concept associated with data privacy has also been advocated across most FinTech regulations. These concepts and considerations have all been engaged with the need to achieve the required ethical requirements. The concept of federated learning is a recently developed measure that achieves the abovementioned concept. It ensured the development of AI and machine learning while keeping privacy in data analysis. The research paper effectively describes the issue of federated learning for confidentiality. It describes the overall process associated with its development and some of the contributions it has achieved. The widespread application of federated learning in FinTech is showcased, and why federated learning is essential for overall growth in FinTech.
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Dissertations / Theses on the topic "Data Privacy in FinTech"

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Zhang, Nan. "Privacy-preserving data mining." [College Station, Tex. : Texas A&M University, 2006. http://hdl.handle.net/1969.1/ETD-TAMU-1080.

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Nguyen, Benjamin. "Privacy-Centric Data Management." Habilitation à diriger des recherches, Université de Versailles-Saint Quentin en Yvelines, 2013. http://tel.archives-ouvertes.fr/tel-00936130.

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This document will focus on my core computer science research since 2010, covering the topic of data management and privacy. More speci cally, I will present the following topics : -ˆ A new paradigm, called Trusted Cells for privacy-centric personal data management based on the Asymmetric Architecture composed of trusted or open (low power) distributed hardware devices acting as personal data servers and a highly powerful, highly available supporting server, such as a cloud. (Chapter 2). ˆ- Adapting aggregate data computation techniques to the Trusted Cells environment, with the example of Privacy-Preserving Data Publishing (Chapter 3). - Minimizing the data that leaves a Trusted Cell, i.e. enforcing the general privacy principle of Limited Data Collection (Chapter 4). This document contains only results that have already been published. As such, rather than focus on the details and technicalities of each result, I have tried to provide an easy way to have a global understanding of the context behind the work, explain the problematic of the work, and give a summary of the main scienti c results and impact.
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Lin, Zhenmin. "Privacy Preserving Distributed Data Mining." UKnowledge, 2012. http://uknowledge.uky.edu/cs_etds/9.

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Privacy preserving distributed data mining aims to design secure protocols which allow multiple parties to conduct collaborative data mining while protecting the data privacy. My research focuses on the design and implementation of privacy preserving two-party protocols based on homomorphic encryption. I present new results in this area, including new secure protocols for basic operations and two fundamental privacy preserving data mining protocols. I propose a number of secure protocols for basic operations in the additive secret-sharing scheme based on homomorphic encryption. I derive a basic relationship between a secret number and its shares, with which we develop efficient secure comparison and secure division with public divisor protocols. I also design a secure inverse square root protocol based on Newton's iterative method and hence propose a solution for the secure square root problem. In addition, we propose a secure exponential protocol based on Taylor series expansions. All these protocols are implemented using secure multiplication and can be used to develop privacy preserving distributed data mining protocols. In particular, I develop efficient privacy preserving protocols for two fundamental data mining tasks: multiple linear regression and EM clustering. Both protocols work for arbitrarily partitioned datasets. The two-party privacy preserving linear regression protocol is provably secure in the semi-honest model, and the EM clustering protocol discloses only the number of iterations. I provide a proof-of-concept implementation of these protocols in C++, based on the Paillier cryptosystem.
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Aron, Yotam. "Information privacy for linked data." Thesis, Massachusetts Institute of Technology, 2013. http://hdl.handle.net/1721.1/85215.

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Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2013.<br>This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.<br>Cataloged from student-submitted PDF version of thesis.<br>Includes bibliographical references (pages 77-79).<br>As data mining over massive amounts of linked data becomes more and more prevalent in research applications, information privacy becomes a more important issue. This is especially true in the biological and medical fields, where information sensitivity is high. Previous experience has shown that simple anonymization techniques, such as removing an individual's name from a data set, are inadequate to fully protect the data's participants. While strong privacy guarantees have been studied for relational databases, these are virtually non-existent for graph-structured linked data. This line of research is important, however, since the aggregation of data across different web sources may lead to privacy leaks. The ontological structure of linked data especially aids these attacks on privacy. The purpose of this thesis is two-fold. The first is to investigate differential privacy, a strong privacy guarantee, and how to construct differentially-private mechanisms for linked data. The second involves the design and implementation of the SPARQL Privacy Insurance Module (SPIM). Using a combination of well-studied techniques, such as authentication and access control, and the mechanisms developed to maintain differential privacy over linked data, it attempts to limit privacy hazards for SPARQL queries. By using these privacy-preservation techniques, data owners may be more willing to share their data sets with other researchers without the fear that it will be misused. Consequently, we can expect greater sharing of information, which will foster collaboration and improve the types of data that researchers can have access to.<br>by Yotam Aron.<br>M. Eng.
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Jawad, Mohamed. "Data privacy in P2P Systems." Nantes, 2011. http://www.theses.fr/2011NANT2020.

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Les communautés en ligne pair-a-pair (P2P), comme les communautés professionnelles (p. Ex. , médicales ou de recherche) deviennent de plus en plus populaires a cause de l’augmentation des besoins du partage de données. Alors que les environnements P2P offrent des caractéristiques intéressantes (p. Ex. , passage a l’échelle, disponibilité, dynamicité), leurs garanties en termes de protection des données sensibles sont limitées. Ils peuvent être considérés comme hostiles car les données publiées peuvent être consultées par tous les pairs (potentiellement malicieux) et utilisées pour tout (p. Ex. , pour le commerce illicite ou tout simplement pour des activités contre les préférences personnelles ou éthiques du propriétaire des données). Cette thèse propose un service qui permet le partage de données sensibles dans les systèmes P2P, tout en assurant leur confidentialité. La première contribution est l’analyse des techniques existant pour la confidentialité de données dans les architectures P2P. La deuxième contribution est un modèle de confidentialité, nomme PriMod, qui permet aux propriétaires de données de spécifier leurs préférences de confidentialité dans de politiques de confidentialité et d’attacher ces politiques a leurs données sensibles. La troisième contribution est le développement de PriServ, un service de confidentialité, base sur une DHT qui met en oeuvre PriMod afin de prévenir la violation de la confidentialité de données. Entre autres, PriServ utilise de techniques de confiance pour prédire le comportement des pairs<br>Online peer-to-peer (P2P) communities such as professional ones (e. G. , medical or research communities) are becoming popular due to increasing needs on data sharing. P2P environments offer valuable characteristics but limited guarantees when sharing sensitive data. They can be considered as hostile because data can be accessed by everyone (by potentially malicious peers) and used for everything (e. G. , for marketing or for activities against the owner’s preferences or ethics). This thesis proposes a privacy service that allows sharing sensitive data in P2P systems while protecting their privacy. The first contribution consists on analyzing existing techniques for data privacy in P2P architectures. The second contribution is a privacy model for P2P systems named PriMod which allows data owners to specify their privacy preferences in privacy policies and to associate them with their data. The third contribution is the development of PriServ, a privacy service located on top of DHT-based P2P systems which implements PriMod to prevent data privacy violations. Among others, PriServ uses trust techniques to predict peers behavior
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Foresti, S. "Preserving privacy in data outsourcing." Doctoral thesis, Università degli Studi di Milano, 2010. http://hdl.handle.net/2434/156360.

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Privacy requirements have an increasing impact on the realization of modern applications. Commercial and legal regulations demand that privacy guarantees be provided whenever sensitive information is stored, processed, or communicated to external parties. Current approaches encrypt sensitive data, thus reducing query execution efficiency and preventing selective information release. In this thesis, we present a comprehensive approach for protecting highly sensitive information when it is stored on systems that are not under the data owner's control. Our approach combines access control and encryption, enforcing access control via structured encryption. Our solution, coupled with efficient algorithms for key derivation and distribution, provides efficient and secure authorization management on outsourced data allowing the data owner to outsource not only the data but the security policy itself. To reduce the amount of data to be encrypted we also investigate data fragmentation as a possible way to protect privacy of data associations and provide fragmentation as a complementary means for protecting privacy: associations broken by fragmentation will be visible only to users authorized (by knowing the proper key) to join fragments. We finally investigate the problem of executing queries over possible data distributed at different servers and which must be controlled to ensure sensitive information and sensitive associations be visible only to parties authorized for that.
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Livraga, G. "PRESERVING PRIVACY IN DATA RELEASE." Doctoral thesis, Università degli Studi di Milano, 2014. http://hdl.handle.net/2434/233324.

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Data sharing and dissemination play a key role in our information society. Not only do they prove to be advantageous to the involved parties, but they can also be fruitful to the society at large (e.g., new treatments for rare diseases can be discovered based on real clinical trials shared by hospitals and pharmaceutical companies). The advancements in the Information and Communication Technology (ICT) make the process of releasing a data collection simpler than ever. The availability of novel computing paradigms, such as data outsourcing and cloud computing, make scalable, reliable and fast infrastructures a dream come true at reasonable costs. As a natural consequence of this scenario, data owners often rely on external storage servers for releasing their data collections, thus delegating the burden of data storage and management to the service provider. Unfortunately, the price to be paid when releasing a collection of data is in terms of unprecedented privacy risks. Data collections often include sensitive information, not intended for disclosure, that should be properly protected. The problem of protecting privacy in data release has been under the attention of the research and development communities for a long time. However, the richness of released data, the large number of available sources, and the emerging outsourcing/cloud scenarios raise novel problems, not addressed by traditional approaches, which need enhanced solutions. In this thesis, we define a comprehensive approach for protecting sensitive information when large collections of data are publicly or selectively released by their owners. In a nutshell, this requires protecting data explicitly included in the release, as well as protecting information not explicitly released but that could be exposed by the release, and ensuring that access to released data be allowed only to authorized parties according to the data owners’ policies. More specifically, these three aspects translate to three requirements, addressed by this thesis, which can be summarized as follows. The first requirement is the protection of data explicitly included in a release. While intuitive, this requirement is complicated by the fact that privacy-enhancing techniques should not prevent recipients from performing legitimate analysis on the released data but, on the contrary, should ensure sufficient visibility over non sensitive information. We therefore propose a solution, based on a novel formulation of the fragmentation approach, that vertically fragments a data collection so to satisfy requirements for both information protection and visibility, and we complement it with an effective means for enriching the utility of the released data. The second requirement is the protection of data not explicitly included in a release. As a matter of fact, even a collection of non sensitive data might enable recipients to infer (possibly sensitive) information not explicitly disclosed but that somehow depends on the released information (e.g., the release of the treatment with which a patient is being cared can leak information about her disease). To address this requirement, starting from a real case study, we propose a solution for counteracting the inference of sensitive information that can be drawn observing peculiar value distributions in the released data collection. The third requirement is access control enforcement. Available solutions fall short for a variety of reasons. Traditional access control mechanisms are based on a reference monitor and do not fit outsourcing/cloud scenarios, since neither the data owner is willing, nor the cloud storage server is trusted, to enforce the access control policy. Recent solutions for access control enforcement in outsourcing scenarios assume outsourced data to be read-only and cannot easily manage (dynamic) write authorizations. We therefore propose an approach for efficiently supporting grant and revoke of write authorizations, building upon the selective encryption approach, and we also define a subscription-based authorization policy, to fit real-world scenarios where users pay for a service and access the resources made available during their subscriptions. The main contributions of this thesis can therefore be summarized as follows. With respect to the protection of data explicitly included in a release, our original results are: i) a novel modeling of the fragmentation problem; ii) an efficient technique for computing a fragmentation, based on reduced Ordered Binary Decision Diagrams (OBDDs) to formulate the conditions that a fragmentation must satisfy; iii) the computation of a minimal fragmentation not fragmenting data more than necessary, with the definition of both an exact and an heuristic algorithms, which provides faster computational time while well approximating the exact solutions; and iv) the definition of loose associations, a sanitized form of the sensitive associations broken by fragmentation that can be safely released, specifically extended to operate on arbitrary fragmentations. With respect to the protection of data not explicitly included in a release, our original results are: i) the definition of a novel and unresolved inference scenario, raised from a real case study where data items are incrementally released upon request; ii) the definition of several metrics to assess the inference exposure due to a data release, based upon the concepts of mutual information, Kullback-Leibler distance between distributions, Pearson’s cumulative statistic, and Dixon’s coefficient; and iii) the identification of a safe release with respect to the considered inference channel and the definition of the controls to be enforced to guarantee that no sensitive information be leaked releasing non sensitive data items. With respect to access control enforcement, our original results are: i) the management of dynamic write authorizations, by defining a solution based on selective encryption for efficiently and effectively supporting grant and revoke of write authorizations; ii) the definition of an effective technique to guarantee data integrity, so to allow the data owner and the users to verify that modifications to a resource have been produced only by authorized users; and iii) the modeling and enforcement of a subscription-based authorization policy, to support scenarios where both the set of users and the set of resources change frequently over time, and users’ authorizations are based on their subscriptions.
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Loukides, Grigorios. "Data utility and privacy protection in data publishing." Thesis, Cardiff University, 2008. http://orca.cf.ac.uk/54743/.

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Data about individuals is being increasingly collected and disseminated for purposes such as business analysis and medical research. This has raised some privacy concerns. In response, a number of techniques have been proposed which attempt to transform data prior to its release so that sensitive information about the individuals contained within it is protected. A:-Anonymisation is one such technique that has attracted much recent attention from the database research community. A:-Anonymisation works by transforming data in such a way that each record is made identical to at least A: 1 other records with respect to those attributes that are likely to be used to identify individuals. This helps prevent sensitive information associated with individuals from being disclosed, as each individual is represented by at least A: records in the dataset. Ideally, a /c-anonymised dataset should maximise both data utility and privacy protection, i.e. it should allow intended data analytic tasks to be carried out without loss of accuracy while preventing sensitive information disclosure, but these two notions are conflicting and only a trade-off between them can be achieved in practice. The existing works, however, focus on how either utility or protection requirement may be satisfied, which often result in anonymised data with an unnecessarily and/or unacceptably low level of utility or protection. In this thesis, we study how to construct /-anonymous data that satisfies both data utility and privacy protection requirements. We propose new criteria to capture utility and protection requirements, and new algorithms that allow A:-anonymisations with required utility/protection trade-off or guarantees to be generated. Our extensive experiments using both benchmarking and synthetic datasets show that our methods are efficient, can produce A:-anonymised data with desired properties, and outperform the state of the art methods in retaining data utility and providing privacy protection.
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Sobati, Moghadam Somayeh. "Contributions to Data Privacy in Cloud Data Warehouses." Thesis, Lyon, 2017. http://www.theses.fr/2017LYSE2020.

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Actuellement, les scénarios d’externalisation de données deviennent de plus en plus courants avec l’avènement de l’infonuagique. L’infonuagique attire les entreprises et les organisations en raison d’une grande variété d’avantages fonctionnels et économiques.De plus, l’infonuagique offre une haute disponibilité, le passage d’échelle et une reprise après panne efficace. L’un des services plus notables est la base de données en tant que service (Database-as-a-Service), où les particuliers et les organisations externalisent les données, le stockage et la gestion `a un fournisseur de services. Ces services permettent de stocker un entrepôt de données chez un fournisseur distant et d’exécuter des analysesen ligne (OLAP).Bien que l’infonuagique offre de nombreux avantages, elle induit aussi des problèmes de s´sécurité et de confidentialité. La solution usuelle pour garantir la confidentialité des données consiste à chiffrer les données localement avant de les envoyer à un serveur externe. Les systèmes de gestion de base de données sécurisés utilisent diverses méthodes de cryptage, mais ils induisent un surcoût considérable de calcul et de stockage ou révèlent des informations sur les données.Dans cette thèse, nous proposons une nouvelle méthode de chiffrement (S4) inspirée du partage secret de Shamir. S4 est un système homomorphique additif : des additions peuvent être directement calculées sur les données cryptées. S4 trait les points faibles des systèmes existants en réduisant les coûts tout en maintenant un niveau raisonnable de confidentialité. S4 est efficace en termes de stockage et de calcul, ce qui est adéquat pour les scénarios d’externalisation de données qui considèrent que l’utilisateur dispose de ressources de calcul et de stockage limitées. Nos résultats expérimentaux confirment l’efficacité de S4 en termes de surcoût de calcul et de stockage par rapport aux solutions existantes.Nous proposons également de nouveaux schémas d’indexation qui préservent l’ordre des données, OPI et waOPI. Nous nous concentrons sur le problème de l’exécution des requêtes exacts et d’intervalle sur des données chiffrées. Contrairement aux solutions existantes, nos systèmes empêchent toute analyse statistique par un adversaire. Tout en assurant la confidentialité des données, les schémas proposés présentent de bonnes performances et entraînent un changement minimal dans les logiciels existants<br>Nowadays, data outsourcing scenarios are ever more common with the advent of cloud computing. Cloud computing appeals businesses and organizations because of a wide variety of benefits such as cost savings and service benefits. Moreover, cloud computing provides higher availability, scalability, and more effective disaster recovery rather than in-house operations. One of the most notable cloud outsourcing services is database outsourcing (Database-as-a-Service), where individuals and organizations outsource data storage and management to a Cloud Service Provider (CSP). Naturally, such services allow storing a data warehouse (DW) on a remote, untrusted CSP and running on-line analytical processing (OLAP).Although cloud data outsourcing induces many benefits, it also brings out security and in particular privacy concerns. A typical solution to preserve data privacy is encrypting data locally before sending them to an external server. Secure database management systems use various encryption schemes, but they either induce computational and storage overhead or reveal some information about data, which jeopardizes privacy.In this thesis, we propose a new secure secret splitting scheme (S4) inspired by Shamir’s secret sharing. S4 implements an additive homomorphic scheme, i.e., additions can be directly computed over encrypted data. S4 addresses the shortcomings of existing approaches by reducing storage and computational overhead while still enforcing a reasonable level of privacy. S4 is efficient both in terms of storage and computing, which is ideal for data outsourcing scenarios that consider the user has limited computation and storage resources. Experimental results confirm the efficiency of S4 in terms of computation and storage overhead with respect to existing solutions.Moreover, we also present new order-preserving schemes, order-preserving indexing (OPI) and wrap-around order-preserving indexing (waOPI), which are practical on cloud outsourced DWs. We focus on the problem of performing range and exact match queries over encrypted data. In contrast to existing solutions, our schemes prevent performing statistical and frequency analysis by an adversary. While providing data privacy, the proposed schemes bear good performance and lead to minimal change for existing software
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Bonatti, Piero A., Bert Bos, Stefan Decker, et al. "Data Privacy Vocabularies and Controls: Semantic Web for Transparency and Privacy." CEUR Workshop Proceedings, 2018. http://epub.wu.ac.at/6490/1/SW4SG_2018.pdf.

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Managing Privacy and understanding the handling of personal data has turned into a fundamental right¿at least for Europeans since May 25th with the coming into force of the General Data Protection Regulation. Yet, whereas many different tools by different vendors promise companies to guarantee their compliance to GDPR in terms of consent management and keeping track of the personal data they handle in their processes, interoperability between such tools as well uniform user facing interfaces will be needed to enable true transparency, user-configurable and -manageable privacy policies and data portability (as also¿implicitly¿promised by GDPR). We argue that such interoperability can be enabled by agreed upon vocabularies and Linked Data.
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Books on the topic "Data Privacy in FinTech"

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Dorfleitner, Gregor, and Lars Hornuf. FinTech and Data Privacy in Germany. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-31335-7.

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Xu, Lei, Chunxiao Jiang, Yi Qian, and Yong Ren. Data Privacy Games. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-77965-2.

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Porter, Kathleen M., and Peter M. Moldave. Data use & privacy. MCLE New England, 2015.

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West, Tobi, and Aeron Zentner. Data Privacy and Governance. SAGE Publications, Inc., 2021. http://dx.doi.org/10.4135/9781071859414.

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Wong, Raymond Chi-Wing, and Ada Wai-Chee Fu. Privacy-Preserving Data Publishing. Springer International Publishing, 2010. http://dx.doi.org/10.1007/978-3-031-01834-3.

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Gkoulalas-Divanis, Aris, and Grigorios Loukides, eds. Medical Data Privacy Handbook. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-23633-9.

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Salomon, David. Data Privacy and Security. Springer New York, 2003. http://dx.doi.org/10.1007/978-0-387-21707-9.

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Makulilo, Alex B., ed. African Data Privacy Laws. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-47317-8.

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Aggarwal, Charu C., and Philip S. Yu, eds. Privacy-Preserving Data Mining. Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-70992-5.

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Torra, Vicenç. Guide to Data Privacy. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-12837-0.

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Book chapters on the topic "Data Privacy in FinTech"

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Dorfleitner, Gregor, and Lars Hornuf. "FinTech Business Models." In FinTech and Data Privacy in Germany. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-31335-7_5.

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Dorfleitner, Gregor, and Lars Hornuf. "FinTechs and Data Protection." In FinTech and Data Privacy in Germany. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-31335-7_3.

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Dorfleitner, Gregor, and Lars Hornuf. "Players in the German FinTech Industry." In FinTech and Data Privacy in Germany. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-31335-7_2.

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Alekseenko, Aleksandr P. "Privacy, Data Protection, and Public Interest Considerations for Fintech." In Global Perspectives in FinTech. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-11954-5_3.

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Dorfleitner, Gregor, and Lars Hornuf. "Introduction." In FinTech and Data Privacy in Germany. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-31335-7_1.

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Dorfleitner, Gregor, and Lars Hornuf. "FinTechs and Data Protection After the Implementation of the GDPR." In FinTech and Data Privacy in Germany. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-31335-7_4.

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Dorfleitner, Gregor, and Lars Hornuf. "Need For Regulation in the German FinTech Market." In FinTech and Data Privacy in Germany. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-31335-7_6.

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Dorfleitner, Gregor, and Lars Hornuf. "Summary in Eleven Theses." In FinTech and Data Privacy in Germany. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-31335-7_7.

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Hornuf, Lars, Sonja Mangold, and Yayun Yang. "Privacy Statements in China, Germany, and the United States." In Data Privacy and Crowdsourcing. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-32064-4_4.

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AbstractThis chapter investigates how crowdsourcing platforms handle matters of data protection and analyzes information from 416 privacy statements. We find that German platforms mostly base their data processing solely on the GDPR, while U.S. platforms refer to numerous international, European, and state-level legal sources on data protection. The Chinese crowdsourcing platforms are usually not open to foreigners and do not refer to the GDPR. The privacy statements provide evidence that some U.S. platforms are specific in the sense that they explicitly state which data are not processed. When we compare the privacy practices of crowdsourcing platforms with the German fintech sector, it is noticeable that pseudonymization and anonymization are, at least in Germany, used much more frequently on crowdsourcing platforms. Most privacy statements did not exhaustively clarify what personal data are shared, even though they mentioned the sharing of data with third parties.
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Zheng, Zhiyong, Kun Tian, and Fengxia Liu. "Fully Homomorphic Encryption." In Financial Mathematics and Fintech. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-7644-5_6.

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AbstractIn 1978, Rivest et al. (1978) proposed the concepts of data bank and fully homomorphic encryption. Some individuals and organizations encrypt the original data and store them in the data bank for privacy protection. Data bank is also called data cloud. Therefore, the cloud stores a large amount of original data, which is obviously a huge wealth. How to use these data effectively? First of all, we must solve the problem of calculation of these encrypted data, which is called a privacy calculation problem. Rivest, Adleman and Dertouzos conjecture that if all data is fully homomorphic encryption, that is, the addition and multiplication of ciphertext are homomorphic to the corresponding addition and multiplication of plaintext, then the encrypted data can be effectively computed by elementary calculation without changing the structure of the plaintext data (under the condition of homomorphism). The RAD conjecture has been proposed for more than 30 years, but no one could solve this problem since the cryptographic structure of the fully homomorphic encryption system is too complicated. In 2009, C. Gentry, a computer scholar at Stanford University, first proposed a fully homomorphic encryption scheme in Gentry (2009b) based on ideal lattice, for which he won the 2022 highest award in theoretical computer science—the Godel Award. Based on Gentry’s work, the second and third fully homomorphic encryption schemes based on LWE distribution and trapdoor matrix technology have also been proposed; see Brakerski and Vaikuntanathan (2011a), (2011b), (2012), (2014), (2015) and Gentry et al. (2013) in 2013. The main purpose of this chapter is to systematically analyze and discuss the above three fully homomorphic encryption techniques, in order to understand the latest research trends of the post-quantum cryptography.
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Conference papers on the topic "Data Privacy in FinTech"

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Pillai, Sanjaikanth E. Vadakkethil Somanathan, and Wen-Chen Hu. "Security and Privacy Challenges and Opportunities in FinTech." In 2024 Cyber Awareness and Research Symposium (CARS). IEEE, 2024. https://doi.org/10.1109/cars61786.2024.10778753.

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Al Balushi, Sara Sadiq, Wilfred Blessing N. R, Nawf Taresh Ghadeer Al-Badi, and Amal Mohammed Al-Maamari. "A Study on Challenges of FinTech for Improving Accounting Information Systems (AIS)." In 2024 2nd International Conference on Computing and Data Analytics (ICCDA). IEEE, 2024. https://doi.org/10.1109/iccda64887.2024.10867391.

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Vasto-Terrientes, Luis, Sergio Martínez, and David Sánchez. "Privacy- & Utility-Preserving Data Releases over Fragmented Data Using Individual Differential Privacy." In 11th International Conference on Information Systems Security and Privacy. SCITEPRESS - Science and Technology Publications, 2025. https://doi.org/10.5220/0013141200003899.

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Yildirim, Simge, Yunus Santur, and Murat Aydogan. "NLP in FinTech: Developing a Lightweight Text-to-Chart Application for Financial Analysis." In 2024 8th International Artificial Intelligence and Data Processing Symposium (IDAP). IEEE, 2024. http://dx.doi.org/10.1109/idap64064.2024.10710766.

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Chatterjee, Pushpita, Debashis Das, and Danda B. Rawat. "A Generative AI Approach for Ensuring Data Integrity Security Resilience in Fintech Systems." In 2024 IEEE 24th International Symposium on Cluster, Cloud and Internet Computing Workshops (CCGridW). IEEE, 2024. http://dx.doi.org/10.1109/ccgridw63211.2024.00027.

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Dekkal, Massilva, Sandrine Prom Tep, Manon Arcand, and Maya Cachecho. "Behind the AI-Scenes: How FinTech Professionals Navigate Regulations and Privacy Concerns to Enhance User Experience." In 13th International Conference on Human Interaction & Emerging Technologies: Artificial Intelligence & Future Applications. AHFE International, 2025. https://doi.org/10.54941/ahfe1005902.

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By 2030, global financial technology (fintech) revenues are expected to surpass $1.5 trillion US dollars, driven by the increasing adoption of digital financial services worldwide (eMarketer, 2023). The rapid advancement of artificial intelligence (AI) has significantly contributed to the fintech industry thrust (Kasmon et al., 2024) which in turn, has radically transformed the financial sector (Sahabuddin et al., 2023; Jang et al., 2021). The literature has established that fintech can be effective in improving how customers experience financial service and product offers (Gupta et al., 2023). Despite the hype over fintech technologies, the successful design and development of fintech solutions is still a challenge for many B2B and B2C businesses (Kasmon et al.,2024), and even more so because ethical and regulation requirements for user protection are key to adoption (Israfilzade and Sadili, 2024; Heeks et al., 2023). Fintech professionals involved with product management play a crucial role as intermediaries between developers and clients in the successful implementation of such digital innovations (Jang et al., 2021; Mogaji and Nguyen, 2021). While financial interactions involve a great deal of sensitive information sharing (e.g., credit card, account number, investments), users become increasingly vulnerable and concerned with their privacy when using fintech applications (Rjoub et al., 2023; Siddik et al., 2023). Several studies have examined the consumer’s perspective when adopting fintech products or services, but very few have investigated the perspective of fintech professionals (Hassan et al., 2023). This research aims to better guide fintech professionals in the design and development of digital fintech solutions, while ensuring adherence to legal requirements for customer protection considering the Canadian financial environment. To do so, this project aims to understand the practices and elements that define the relationship between fintech companies and their customers. This study relied on semi-structured interviews conducted with six fintech professionals involved with the design, development, regulation compliance, and governance of AI/digital solutions in the financial sector (4 in B2B and 2 in B2C; 4 men and 2 women). Participants were professionally titled either as CEOs, lawyers specialized in AI digital governance and fintech director. The discussion guide covered three main topics: their relationship with their clients, regulations constraints and best practices. Interviews were virtually conducted and transcribed. NVIVO was used for data categorization and coding and the qualitative analysis followed the procedure advocated by Gioia et al. (2013) to ensure qualitative rigor. The findings show that (1) compliance is central to fintech, with significant resources being invested in ensuring legal adherence and transparency (2) striking a balance between innovation and reliability is a challenge to maintain customer relationship and (3) focusing on privacy by design is a key concern, since customers are demanding higher levels of clarity, transparency and control over their personal data without compromising on the user experience. This study makes a significant contribution to the understanding of the fintech specific practices and challenges, recommending that fintech firms adopt detailed privacy policies to govern, manage, share and properly secure data to meet regulatory as well as customer expectations.
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Khuan, Hendri. "Shielding Privacy: Navigating Personal Data Protection Law in Indonesian Fintech Peer-to-Peer Lending." In Proceedings of the First International Cyber Law Conference, ICL-C 2023, 11 November 2023, Jakarta, Indonesia. EAI, 2025. https://doi.org/10.4108/eai.11-11-2023.2351308.

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Căpățîna (Dumitrache), Cristina S., and Dragoș Bîlteanu. "Digital Currencies: Individual Perceptions of the Impact on Money Laundering and the Transition to a Cashless Environment." In 4th International Conference on FinTech, Cyberspace and Artificial Intelligence Law. ADJURIS – International Academic Publisher, 2024. http://dx.doi.org/10.62768/adjuris/2024/1/05.

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Based on the correlation between the use of cash and criminal activity demonstrated in the literature, we conducted a survey to identify the civilian community’s perception of the extent to which the adoption of cashless transactions could mitigate criminal behaviour. Our study investigates both attitudes towards digital currencies and the feasibility of transitioning to a cashless society. The survey results show scepticism towards limiting cash as a comprehensive solution to combat illicit financial activities, highlighting the importance for policymakers to weigh the potential benefits against criminal adaptability. The varied perspectives among legal and public policy respondents highlight the nuanced considerations surrounding cash restrictions, with some advocating their benefits in combating money laundering while others remain sceptical. Concerns expressed by respondents about privacy, institutional control and economic autonomy highlight the multiple implications of the transition to a cashless society. These findings underline the need for robust legal and regulatory frameworks to protect individual privacy rights and ensure transparency in the use of transaction data. In addition, respondents’ concerns about oversight and trust in digital payment systems underscore the need for thorough analysis prior to the adoption of centralised digital currencies.
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Paraschiv, Carmen Silvia. "Study on Digital Transformation and Algorithmic Law." In 4th International Conference on FinTech, Cyberspace and Artificial Intelligence Law. ADJURIS – International Academic Publisher, 2024. http://dx.doi.org/10.62768/adjuris/2024/1/06.

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The article studies the interaction between digital transformation and the legal field, analyzing the impact of digital technologies on legislation and legal practice. After outlining the basics of digital transformation, it examines how technological evolution affects the rule of law and the legal implications of digital transformation, with a focus on data protection and privacy in the digital age. Emerging legal tools such as smart contracts and blockchain technology present challenges and opportunities. Access to justice in the digital age is analyzed, noting the influence of technology on legal processes and online dispute resolution platforms. The paper also addresses the impact of digital transformation on legal education and the ethical issues associated with the use of technology in legal practice. In conclusion, the paper emphasizes the importance of adapting the legal system and educational practices to the changes generated by the digital transformation.
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Oprea, Isabelle, and Daniela Duță. "Integrating AI in Bank Digitalization: Strategies, Challenges and Future Perspectives." In 4th International Conference on FinTech, Cyberspace and Artificial Intelligence Law. ADJURIS – International Academic Publisher, 2024. http://dx.doi.org/10.62768/adjuris/2024/1/13.

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The paper delves into the burgeoning role of artificial intelligence (AI) within the realm of banking digitalization. It begins by contextualizing the necessity for banks to adapt to digital transformation, driven by the increasing demand for efficient, personalized banking services and the pressure of fintech competitors. The core of the paper is dedicated to discussing the multifaceted strategies that banks are employing to integrate AI technologies, including automated customer service, fraud detection algorithms, and personalized financial advice systems. Moreover, the paper highlights significant challenges banks face in this integration process, such as data privacy concerns, and the need for substantial investment in technology and employees’ training. The issue of a potential digital divide and its implications for customer access to banking services is also explored. Future perspectives are optimistically outlined, emphasizing AI’s potential to revolutionize banking by further enhancing customer experience, optimizing operational efficiency, and fostering financial inclusion. The article argues that with thoughtful regulation, continuous innovation, and a focus on AI use, the integration of AI into banking can lead to more resilient and customer-centric financial institutions.
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Reports on the topic "Data Privacy in FinTech"

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Wijaya, Trissia. Unpacking The Fintech Regulatory Sandbox Framework in Indonesia: Risks Management and The Data Privacy Imperative. Center for Indonesian Policy Studies, 2023. http://dx.doi.org/10.35497/565201.

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Liu, Zhuang, Michael Sockin, and Wei Xiong. Data Privacy and Temptation. National Bureau of Economic Research, 2020. http://dx.doi.org/10.3386/w27653.

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Maggio, Marco Di, Dimuthu Ratnadiwakara, and Don Carmichael. Invisible Primes: Fintech Lending with Alternative Data. National Bureau of Economic Research, 2022. http://dx.doi.org/10.3386/w29840.

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Zhan, Zhijun, and LiWu Chang. Privacy-Preserving Collaborative Data Mining. Defense Technical Information Center, 2003. http://dx.doi.org/10.21236/ada464602.

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Heffetz, Ori, and Katrina Ligett. Privacy and Data-Based Research. National Bureau of Economic Research, 2013. http://dx.doi.org/10.3386/w19433.

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Liu, Zhuang, Michael Sockin, and Wei Xiong. Data Privacy and Algorithmic Inequality. National Bureau of Economic Research, 2023. http://dx.doi.org/10.3386/w31250.

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Kukutai, Tahu, Shemana Cassim, Vanessa Clark, et al. Māori data sovereignty and privacy. Te Ngira Institute for Population Research, 2023. https://doi.org/10.15663/j21.35481.

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Privacy is a fundamental human right. One of its most important aspects is information privacy – providing individuals with control over the way in which their personal data is collected, used, disclosed and otherwise handled. Existing information privacy regulation neither recognises nor protects the collective privacy rights of Indigenous peoples. This paper explores Indigenous data privacy, and the challenges and opportunities, in the context of Aotearoa. It has two aims: to identify gaps in existing data privacy approaches with regards to Indigenous data, and to provide a foundation for progressing alternative privacy paradigms. We argue that while personal data protection is necessary, it is insufficient to meet the needs of Māori and Aotearoa more broadly. In so doing, we draw on three areas of research: Indigenous and Māori data sovereignty; data and information privacy, including collective privacy; and Māori and Indigenous privacy perspectives. We examine key features of the Aotearoa privacy context – including the Privacy Act 2020 (NZ) – and consider the implications of te Tiriti o Waitangi and tikanga Māori for alternative privacy approaches. Future options, including legal and extra-legal measures, are proposed.
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Riyadi, Gliddheo. Data Privacy in the Indonesian Personal Data Protection Legislation. Center for Indonesian Policy Studies, 2021. http://dx.doi.org/10.35497/341482.

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Esponda, Fernando, Stephanie Forrest, and Paul Helman. Enhancing Privacy through Negative Representations of Data. Defense Technical Information Center, 2004. http://dx.doi.org/10.21236/ada498766.

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Chen, Long, Yadong Huang, Shumiao Ouyang, and Wei Xiong. The Data Privacy Paradox and Digital Demand. National Bureau of Economic Research, 2021. http://dx.doi.org/10.3386/w28854.

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