Relevant bibliographies by topics / Big data privacy

Contents

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

Academic literature on the topic 'Big data privacy'

Author: Grafiati
Published: 4 June 2025
Last updated: 1 August 2025

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Journal articles on the topic "Big data privacy"

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Habegger, Benjamin. "Big Data vs. Privacy Big Data." Services Transactions on Big Data 1, no. 1 (2014): 25–35. http://dx.doi.org/10.29268/stbd.2014.1.1.3.

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Scotti, Veronica. "Big data or big (privacy) problem?" IEEE Instrumentation & Measurement Magazine 20, no. 5 (2017): 23–26. http://dx.doi.org/10.1109/mim.2017.8036692.

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Gaff, Brian M., Heather Egan Sussman, and Jennifer Geetter. "Privacy and Big Data." Computer 47, no. 6 (2014): 7–9. http://dx.doi.org/10.1109/mc.2014.161.

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Kapil, Gayatri, Alka Agrawal, and R. A. Khan. "Big Data Security and Privacy Issues." Asian Journal of Computer Science and Technology 7, no. 2 (2018): 128–32. http://dx.doi.org/10.51983/ajcst-2018.7.2.1861.

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Big data gradually become a hot topic of research and business and has been growing at exponential rate. It is a combination of structured, semi-structured & unstructured data which is generated constantly through various sources from different platforms like web servers, mobile devices, social network, private and public cloud etc. Big data is used in many organisations and enterprises, big data security and privacy have been increasingly concerned. However, there is a clear contradiction between the large data security and privacy and the widespread use of big data. In this paper, we hav
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Basha, M. John, T. Satyanarayana Murthy, A. S. Valarmathy, et al. "Privacy-Preserving Data Mining and Analytics in Big Data." E3S Web of Conferences 399 (2023): 04033. http://dx.doi.org/10.1051/e3sconf/202339904033.

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Privacy concerns have gotten more attention as Big Data has spread. The difficulties of striking a balance between the value of data and individual privacy have led to the emergence of privacy-preserving data mining and analytics approaches as a crucial area of research. An overview of the major ideas, methods, and developments in privacy-preserving data mining and analytics in the context of Big Data is given in this abstract. Data mining that protects privacy tries to glean useful insights from huge databases while shielding the private data of individuals. Commonly used in traditional data
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Winarsih, Winarsih, and Irwansyah Irwansyah. "PROTEKSI PRIVASI BIG DATA DALAM MEDIA SOSIAL." Jurnal Audience 3, no. 1 (2020): 1–33. http://dx.doi.org/10.33633/ja.v3i1.3722.

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AbstrakPerkembangan media sosial di Indonesia begitu pesat dengan jumlah pengguna yang terus meningkat. Akan tetapi hal tersebut kurang diimbangi dengan kesadaran tentang privasi dalam kaitannya dengan big data yang dihasilkan oleh penyedia layanan. Penyedia layanan memberikan kebijakan berupa syarat dan ketentuan akan tetapi masyarakat umumnya masih rendah dalam hal memiliki kesadaran tentang privasi data pribadi mereka. Penelitian ini bertujuan untuk mengetahui solusi dari permasalahan privasi big data dalam media sosial dan dianalisis dengan teori privasi komunikasi. Metode yang digunakan d
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Abdul Manap, Nazura, Mohamad Rizal Abd Rahman, and Siti Nur Farah Atiqah Salleh. "HEALTH DATA OWNERSHIP IN MALAYSIA PUBLIC AND PRIVATE HEALTHCARE: A LEGAL ANALYSIS OF HEALTH DATA PRIVACY IN THE AGE OF BIG DATA." International Journal of Law, Government and Communication 7, no. 30 (2022): 33–41. http://dx.doi.org/10.35631/ijlgc.730004.

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Health data ownership in big data is a new legal issue. The problem stands between the public and private healthcare as the main proprietor of health data. In Malaysia, health data ownership is under government hospitals and private healthcare jurisdictions. Who owns the data will be responsible for safeguarding it, including its privacy. Various technical methods are applied to protect health data, such as aggregation and anonymization. The thing is, do these technical methods are still reliable to safeguard privacy in big data? In terms of legal protection, private healthcare is governed und
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M., Santhiya Devi*1 &. Dr.K.Arunesh2. "PRIVACY PRESERVATION TECHNIQUES FOR PERSONALIZED DATA IN BIG DATA." GLOBAL JOURNAL OF ENGINEERING SCIENCE AND RESEARCHES 6, no. 4 (2019): 335–40. https://doi.org/10.5281/zenodo.2653603.

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The recent advancements in this digital world huge amount of information are generated and shared, and the management of such large data is the most difficult and challenging task. Due to its size and variety of data, its name big data was derived. In the management of this data, some information may be disclosed. This type of disclosure can lead to leakage of Personal Identifiable Information (PII), as it contains individual’s information. The voluminous data generated from the various sources can be processed and analyzed to support decision making. However, data analytics is prone to
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Lazar, Nicole. "The Big Picture: Big Data and Privacy." CHANCE 28, no. 1 (2015): 39–42. http://dx.doi.org/10.1080/09332480.2015.1016848.

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Lazar, Nicole. "The Big Picture: Big Data and Privacy." CHANCE 32, no. 1 (2019): 55–58. http://dx.doi.org/10.1080/09332480.2019.1579589.

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Dissertations / Theses on the topic "Big data privacy"

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Sang, Lin. "Social Big Data and Privacy Awareness." Thesis, Uppsala universitet, Institutionen för informatik och media, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-242444.

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Based on the rapid development of Big Data, the data from the online social network becomea major part of it. Big data make the social networks became data-oriented rather than social-oriented. Taking this into account, this dissertation presents a qualitative study to research howdoes the data-oriented social network affect its users’ privacy management for nowadays. Within this dissertation, an overview of Big Data and privacy issues on the social network waspresented as a background study. We adapted the communication privacy theory as a frameworkfor further analysis how individuals manage t
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Ainslie, Mandi. "Big data and privacy : a modernised framework." Diss., University of Pretoria, 2017. http://hdl.handle.net/2263/59805.

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Like the revolutions that preceded it, the Fourth Industrial Revolution has the potential to raise global income levels and improve the quality of life for populations around the world. Responding to global challenges, generating efficiencies, prediction improvement, democratisation access to information and empowering individuals are a few examples of the economic and social value created by personal information. However, this technological innovation, efficiency and productivity comes at a price -?privacy. As a result, individuals are growingly concerned that companies and governments are n
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FAVALE, THOMAS. "Strengthening Privacy and Cybersecurity through Anonymization and Big Data." Doctoral thesis, Politecnico di Torino, 2023. https://hdl.handle.net/11583/2975701.

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Liu, Lian. "PRIVACY PRESERVING DATA MINING FOR NUMERICAL MATRICES, SOCIAL NETWORKS, AND BIG DATA." UKnowledge, 2015. http://uknowledge.uky.edu/cs_etds/31.

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Motivated by increasing public awareness of possible abuse of confidential information, which is considered as a significant hindrance to the development of e-society, medical and financial markets, a privacy preserving data mining framework is presented so that data owners can carefully process data in order to preserve confidential information and guarantee information functionality within an acceptable boundary. First, among many privacy-preserving methodologies, as a group of popular techniques for achieving a balance between data utility and information privacy, a class of data perturbati
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Vu, Xuan-Son. "Privacy-awareness in the era of Big Data and machine learning." Licentiate thesis, Umeå universitet, Institutionen för datavetenskap, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-162182.

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Social Network Sites (SNS) such as Facebook and Twitter, have been playing a great role in our lives. On the one hand, they help connect people who would not otherwise be connected before. Many recent breakthroughs in AI such as facial recognition [49] were achieved thanks to the amount of available data on the Internet via SNS (hereafter Big Data). On the other hand, due to privacy concerns, many people have tried to avoid SNS to protect their privacy. Similar to the security issue of the Internet protocol, Machine Learning (ML), as the core of AI, was not designed with privacy in mind. For i
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Shaham, Sina. "Location Privacy in the Era of Big Data and Machine Learning." Thesis, The University of Sydney, 2019. https://hdl.handle.net/2123/21689.

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Location data of individuals is one of the most sensitive sources of information that once revealed to ill-intended individuals or service providers, can cause severe privacy concerns. In this thesis, we aim at preserving the privacy of users in telecommunication networks against untrusted service providers as well as improving their privacy in the publication of location datasets. For improving the location privacy of users in telecommunication networks, we consider the movement of users in trajectories and investigate the threats that the query history may pose on location privacy. We deve
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MARANDOLA, Daniele. "Le categorie giuridiche dinanzi alla sfida dei Big Data." Doctoral thesis, Università degli studi di Cassino, 2020. http://hdl.handle.net/11580/75275.

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Big data is a term for massive data sets having large, more varied and complexstructure with the difficulties of storing, analyzing and visualizing for furtherprocesses or results. The process of researchinto massive amounts of data to revealhiddenpatterns and secret correlationsnamedas big data analytics. These useful informations for companies or organizations with the help of gainingricher and deeper insights and getting an advantage over the competition. For thisreason, big data implementationsneed to be analyzed and executedasaccuratelyaspossible. Thispaperpresents an overview of big data
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Huang, Xueli. "Achieving Data Privacy and Security in Cloud." Diss., Temple University Libraries, 2016. http://cdm16002.contentdm.oclc.org/cdm/ref/collection/p245801coll10/id/372805.

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Computer and Information Science<br>Ph.D.<br>The growing concerns in term of the privacy of data stored in public cloud have restrained the widespread adoption of cloud computing. The traditional method to protect the data privacy is to encrypt data before they are sent to public cloud, but heavy computation is always introduced by this approach, especially for the image and video data, which has much more amount of data than text data. Another way is to take advantage of hybrid cloud by separating the sensitive data from non-sensitive data and storing them in trusted private cloud and un-trus
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Michel, Axel. "Personalising privacy contraints in Generalization-based Anonymization Models." Thesis, Bourges, INSA Centre Val de Loire, 2019. http://www.theses.fr/2019ISAB0001/document.

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Les bénéfices engendrés par les études statistiques sur les données personnelles des individus sont nombreux, que ce soit dans le médical, l'énergie ou la gestion du trafic urbain pour n'en citer que quelques-uns. Les initiatives publiques de smart-disclosure et d'ouverture des données rendent ces études statistiques indispensables pour les institutions et industries tout autour du globe. Cependant, ces calculs peuvent exposer les données personnelles des individus, portant ainsi atteinte à leur vie privée. Les individus sont alors de plus en plus réticent à participer à des études statistique
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METWALLEY, HASSAN. "Big Data Methodologies and Applications to Privacy and Web Tracking in the Internet." Doctoral thesis, Politecnico di Torino, 2017. http://hdl.handle.net/11583/2667668.

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While on the Internet, individuals encounter invisible services that collect personal information, also known as third-party Web trackers (trackers for short). Linked to advertisement, social sharing, and analytic services in general, hundreds of companies de facto track and build profiles of people. Therefore, actually individuals leak personal and corporate information to trackers whose (legitimate or not) businesses revolve around the value of collected data. The implications are serious, from a person unwillingly exposing private information to an unknown third-party, to a company being un
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Books on the topic "Big data privacy"

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Price, Tom. Big Data and Privacy. CQ Press, 2013. http://dx.doi.org/10.4135/cqresrre20131025.

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E, Ludloff Mary, ed. Privacy and big data. O'Reilly Media, 2011.

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Attoh-Okine, Nii O. Big Data and Differential Privacy. John Wiley & Sons, Inc., 2017. http://dx.doi.org/10.1002/9781119229070.

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Choo, Kim-Kwang Raymond, and Ali Dehghantanha, eds. Handbook of Big Data Privacy. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-38557-6.

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Qu, Youyang, Mohammad Reza Nosouhi, Lei Cui, and Shui Yu. Personalized Privacy Protection in Big Data. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-3750-6.

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Das, Pradip Kumar, Hrudaya Kumar Tripathy, and Shafiz Affendi Mohd Yusof, eds. Privacy and Security Issues in Big Data. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-1007-3.

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Torra, Vicenç. Data Privacy: Foundations, New Developments and the Big Data Challenge. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-57358-8.

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Jiang, Richard, Ahmed Bouridane, Chang-Tsun Li, et al., eds. Big Data Privacy and Security in Smart Cities. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-04424-3.

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Pan, Miao, Jingyi Wang, Sai Mounika Errapotu, Xinyue Zhang, Jiahao Ding, and Zhu Han. Big Data Privacy Preservation for Cyber-Physical Systems. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-13370-2.

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Lin, Limei, Yuhong Liu, and Chia-Wei Lee, eds. Security and Privacy in Social Networks and Big Data. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-7913-1.

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Book chapters on the topic "Big data privacy"

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Torra, Vicenç, Guillermo Navarro-Arribas, and Klara Stokes. "Data Privacy." In Studies in Big Data. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-97556-6_7.

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Kulesza, Joanna. "Privacy." In Encyclopedia of Big Data. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-319-32010-6_172.

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Kulesza, Joanna. "Privacy." In Encyclopedia of Big Data. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-32001-4_172-1.

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Torra, Vicenç. "User’s Privacy." In Studies in Big Data. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-57358-8_4.

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Mortazavi, Masood, and Khaled Salah. "Privacy and Big Data." In Computer Communications and Networks. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-08470-1_3.

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Lipschultz, Jeremy Harris. "Big Data and Privacy." In Social Media Communication, 4th ed. Routledge, 2023. http://dx.doi.org/10.4324/9781003281924-8.

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Hota, Jhalak, Deepak Puthal, and Abhay Kumar Samal. "Privacy Cube." In Encyclopedia of Big Data Technologies. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-63962-8_242-1.

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Hota, Jhalak, Deepak Puthal, and Abhay Kumar Samal. "Privacy Cube." In Encyclopedia of Big Data Technologies. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-319-77525-8_242.

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Hammond-Errey, Miah. "Data and Privacy." In Big Data, Emerging Technologies and Intelligence. Routledge, 2023. http://dx.doi.org/10.4324/9781003389651-6.

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Ebeling, Mary F. E. "Privacy and Data Phantoms." In Healthcare and Big Data. Palgrave Macmillan US, 2016. http://dx.doi.org/10.1057/978-1-137-50221-6_3.

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Conference papers on the topic "Big data privacy"

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Kabou, Salheddine, Laid Gasmi, and Abdelbaset Kabou. "Privacy Preserving Continuous Big Data Publishing." In 2024 4th International Conference on Embedded & Distributed Systems (EDiS). IEEE, 2024. https://doi.org/10.1109/edis63605.2024.10783344.

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Chathoth, Ajesh Koyatan, Clark P. Necciai, Abhyuday Jagannatha, and Stephen Lee. "Differentially Private Federated Continual Learning with Heterogeneous Cohort Privacy." In 2022 IEEE International Conference on Big Data (Big Data). IEEE, 2022. http://dx.doi.org/10.1109/bigdata55660.2022.10021082.

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Jung, Kangsoo, and Seog Park. "Privacy Bargaining with Fairness: Privacy-Price Negotiation System for Applying Differential Privacy in Data Market Environments." In 2019 IEEE International Conference on Big Data (Big Data). IEEE, 2019. http://dx.doi.org/10.1109/bigdata47090.2019.9006101.

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Takagi, Shun, Fumiyuki Kato, Yang Cao, and Masatoshi Yoshikawa. "Asymmetric Differential Privacy." In 2022 IEEE International Conference on Big Data (Big Data). IEEE, 2022. http://dx.doi.org/10.1109/bigdata55660.2022.10020709.

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Zhong, Haoti, Hao Li, Anna Squicciarini, Sarah Rajtmajer, and David Miller. "Toward Image Privacy Classification and Spatial Attribution of Private Content." In 2019 IEEE International Conference on Big Data (Big Data). IEEE, 2019. http://dx.doi.org/10.1109/bigdata47090.2019.9006510.

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Ushiyama, Shojiro, Tsubasa Takahashi, Masashi Kudo, and Hayato Yamana. "Homomorphic Encryption-Friendly Privacy-Preserving Partitioning Algorithm for Differential Privacy." In 2022 IEEE International Conference on Big Data (Big Data). IEEE, 2022. http://dx.doi.org/10.1109/bigdata55660.2022.10020699.

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Cerisara, Christophe, and Alfredo Cuzzocrea. "Unsupervised Risk for Privacy." In 2021 IEEE International Conference on Big Data (Big Data). IEEE, 2021. http://dx.doi.org/10.1109/bigdata52589.2021.9671539.

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Fedeli, Stefano, Frida Schain, Sana Imtiaz, Zainab Abbas, and Vladimir Vlassov. "Privacy Preserving Survival Prediction." In 2021 IEEE International Conference on Big Data (Big Data). IEEE, 2021. http://dx.doi.org/10.1109/bigdata52589.2021.9672036.

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Bertino, Elisa. "Big data security and privacy." In 2016 IEEE International Conference on Big Data (Big Data). IEEE, 2016. http://dx.doi.org/10.1109/bigdata.2016.7840581.

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Shrivastva, Krishna Mohan Pd, M. A. Rizvi, and Shailendra Singh. "Big Data Privacy Based on Differential Privacy a Hope for Big Data." In 2014 International Conference on Computational Intelligence and Communication Networks (CICN). IEEE, 2014. http://dx.doi.org/10.1109/cicn.2014.167.

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Reports on the topic "Big data privacy"

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Vincent, Charles, Madjid Tavana, and Tatiana Gherman. The Right To Be Forgotten – Is Privacy Sold Out in the Big Data Age? CENTRUM Catolica Graduate Business School, 2014. http://dx.doi.org/10.7835/ccwp-2014-02-0006.

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van der Sloot, Bart. The Quality of Life: Protecting Non-personal Interests and Non-personal Data in the Age of Big Data. Universitätsbibliothek J. C. Senckenberg, Frankfurt am Main, 2021. http://dx.doi.org/10.21248/gups.64579.

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Under the current legal paradigm, the rights to privacy and data protection provide natural persons with subjective rights to protect their private interests, such as related to human dignity, individual autonomy and personal freedom. In principle, when data processing is based on non-personal or aggregated data or when such data pro- cesses have an impact on societal, rather than individual interests, citizens cannot rely on these rights. Although this legal paradigm has worked well for decades, it is increasingly put under pressure because Big Data processes are typically based indis- crimin
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Lampkin, Cheryl. Privacy, Storage, and Usage: A Look at How Older Adults View Big Data in Health Care. AARP Research, 2021. http://dx.doi.org/10.26419/res.00457.001.

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Stucke, Maurice E. AI, Antitrust & Privacy. Institute for New Economic Thinking Working Paper Series, 2025. https://doi.org/10.36687/inetwp236.

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Generative artificial intelligence (AI) is reshaping how companies profile individuals, create and target ads, and influence behavior—often in ways that undermine privacy, autonomy, and democracy. This article explores a critical but overlooked question: how AI affects the relationship between competition and privacy. Increased competition in the AI supply chain may seem like a solution to Big Tech’s dominance, but when firms are rewarded for surveillance and manipulation, more competition can actually make things worse. Drawing on recent market trends and twenty state privacy laws, the Articl
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Guicheney, William, Tinashe Zimani, Hope Kyarisiima, and Louisa Tomar. Big Data in the Public Sector: Selected Applications and Lessons Learned. Inter-American Development Bank, 2016. http://dx.doi.org/10.18235/0007024.

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This paper analyzes different ways in which big data can be leveraged to improve the efficiency and effectiveness of government. It describes five cases where massive and diverse sets of information are gathered, processed, and analyzed in three different policy areas: smart cities, taxation, and citizen security. The cases, compiled from extensive desk research and interviews with leading academics and practitioners in the field of data analytics, have been analyzed from the perspective of public servants interested in big data and thus address both the technical and the institutional aspects
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Vaz, Maria João, and Helena Machado. A systematic literature review of Big Data in tourism industry: a state of the art and future directions. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, 2022. http://dx.doi.org/10.37766/inplasy2022.5.0012.

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Review question / Objective: P.E.O: Population, exposure, outcome. What privacy and data protection challenges are linked by different stakeholders, to the Big Data's application in the tourism sector: P - stakeholders; E - Big Data in tourism; O - privacy and data protection challenges. Condition being studied: This investigation aims to map the social and ethical controversies associated with the use of Big Data, addressing the “technological optimism” that tends to surround the use of these techniques in the tourism sector, which may compromise sustainable tourism in the long term. Main out
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Greenberg, Jane, Samantha Grabus, Florence Hudson, et al. The Northeast Big Data Innovation Hub: "Enabling Seamless Data Sharing in Industry and Academia" Workshop Report. Drexel University, 2017. http://dx.doi.org/10.17918/d8159v.

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Increasingly, both industry and academia, in fields ranging from biology and social sciences to computing and engineering, are driven by data (Provost &amp; Fawcett, 2013; Wixom, et al, 2014); and both commercial success and academic impact are dependent on having access to data. Many organizations collecting data lack the expertise required to process it (Hazen, et al, 2014), and, thus, pursue data sharing with researchers who can extract more value from data they own. For example, a biosciences company may benefit from a specific analysis technique a researcher has developed. At the same tim
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Zard, Lex. Consent, Pay or Settle: Meta’s Struggle for Staying Profitable in the European Union. Balsillie School of International Affairs, 2025. https://doi.org/10.51644/bcs008.

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Meta’s shift from the dual option of the “consent-or-pay” model to the triple option of personalized ads, subscription with no ads, and less personalized ads, highlights the challenges of balancing consumer privacy and profitability in digital markets. The company historically relied on surveillance advertising across platforms such as Facebook and Instagram, tracking users’ behaviour to create targeted ads. This highly lucrative model helped Meta amass almost US$132 billion in advertising revenue in 2023. Meta’s consent-or-pay model embodies the conflict between data monetization and consumer
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Marsden, Eric, and Véronique Steyer. Artificial intelligence and safety management: an overview of key challenges. Foundation for an Industrial Safety Culture, 2025. https://doi.org/10.57071/iae290.

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Artificial intelligence based on deep learning, along with big data analysis, has in recent years been the subject of rapid scientific and technological advances. These technologies are increasingly being integrated into various work environments with the aim of enhancing performance and productivity. This dimension of the digital transformation of businesses and regulatory authorities presents both significant opportunities and potential risks for industrial safety management practices. While there are numerous expected benefits, such as the ability to process large volumes of reliability dat
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Ampatzidis, Yiannis, Mahendra Bhandari, Andres Ferreyra, et al. AI in Agriculture: Opportunities, Challenges, and Recommendations. Chair Alex Thomasson. Council for Agricultural Science and Technology, 2025. https://doi.org/10.62300/iaag042514.

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Artificial Intelligence (AI) is rapidly being integrated into people’s lives, reshaping industries, and enabling previously unimagined innovation, even in agriculture. Generative AI focuses on creating content like text and pictures based on vast quantities of data. ExtensionBot is a generative AI platform that supports agricultural extension by providing farmers with accurate scientific information and specific recommendations. It has been shown to deliver more accurate responses to agricultural questions than broader generative AI models. Other forms of AI have been used to analyze data to p
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