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Relevant bibliographies by topics / And Data Privacy

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Academic literature on the topic 'And Data Privacy'

Author: Grafiati

Published: 5 June 2025

Last updated: 2 August 2025

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Journal articles on the topic "And Data Privacy"

1

Gade, Kishore. "Data Analytics: Data Privacy, Data Ethics, Data Monetization." International Journal of Science and Research (IJSR) 9, no. 2 (2020): 1953–59. http://dx.doi.org/10.21275/sr20027110931.

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Yerbulatov, Sultan. "Data Security and Privacy in Data Engineering." International Journal of Science and Research (IJSR) 13, no. 4 (2024): 232–36. http://dx.doi.org/10.21275/es24318121241.

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R.Mahesh and Dr.T.Meyyappan. "A New Method for Preserving Privacy in Data Publishing Against Attribute and Identity Disclosure Risk." International Journal on Cryptography and Information Security (IJCIS) 3, no. 2 (2020): 23–30. https://doi.org/10.5281/zenodo.4013312.

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Sharing, transferring, mining and publishing data are fundamental operations in day to day life. Preserving the privacy of individuals is essential one. Sensitive personal information must be protected when data are published. There are two kinds of risks namely attribute disclosure and identity disclosure that affects privacy of individuals whose data are published. Early Researchers have contributed new methods namely k-anonymity, l-diversity, t-closeness to preserve privacy. K-anonymity method preserves privacy of individuals against identity disclosure attack alone. But Attribute disclosur

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P, Ram Mohan Rao, Murali Krishna S, and P. Siva Kumar A. "Novel algorithm for efficient privacy preservation in data analytics." Indian Journal of Science and Technology 14, no. 6 (2021): 519–26. https://doi.org/10.17485/IJST/v14i6.1773.

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Abstract <strong>Objective:</strong> To address the modern privacy threats in data analytics by designing an efficient privacy preserving data analytics technique.<strong> Methods:</strong> The method applied is a non anonymized method that uses the concepts of synthesizing quasi identifiers and application of differential privacy. The proposed method was applied to three data sets viz. Adult data set, Statlogdata set and Indian Liver Patient data set. All the data sets are freely available in the UCI repository. Findings: The study presents “Synthesize Quasi Identifiers a

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Torra, Vicenç, and Guillermo Navarro-Arribas. "Data privacy." Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 4, no. 4 (2014): 269–80. http://dx.doi.org/10.1002/widm.1129.

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Hassan, Jamal, A. Algeelani Nasir, and Al-Sammarraie Najeeb. "Advance Data-Privacy by Using Artificial Intelligence." International Journal of Computer Science and Information Technology Research 10, no. 4 (2022): 38–45. https://doi.org/10.5281/zenodo.7398762.

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<strong>Abstract:</strong> With the progression utilize of computers broadly, utilization of the information has moreover developed to a huge level. Nowadays information is collected without any reason, and each action of a machine or a human being is recorded, On the off chance that required in the future, at that point, the information will be dissected but here the address of believe emerges as the information will go through numerous stages for the investigation by distinctive parties. The information may contain a few touchy or private data which can be mutualized by the organizations inc

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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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Krishna, Prasanth Brahmaji Kanagarla. "The Role of Synthetic Data in Ensuring Data Privacy and Enabling Secure Analytics." European Journal of Advances in Engineering and Technology 11, no. 10 (2024): 75–79. https://doi.org/10.5281/zenodo.14012320.

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This research focuses on the analysis of the role synthetic data could play in the maintenance of privacy under the GDPR regulations. It considers, for actual effectiveness, some of the methods for generating synthetic data, like differential privacy and GANs. The study also shows some challenges to organizations about compliance with the data, since the main use of synthetic data is analytical. The performance of the experiments revealed that synthetic data represented a good balance regarding the protection of privacy. This presented the assumption that new methods are, above all, the key to

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Mrs., M. S. Lakshmi Devi. "Privacy-Preserving Data Fragmentation and Aggregation." Journal of Scholastic Engineering Science and Management 2, no. 9 (2023): 23–30. https://doi.org/10.5281/zenodo.8311026.

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<strong>Privacy-preserving data fragmentation and aggregation techniques aim to preserve the privacy of individual data points while still allowing for the aggregation of data for analysis. This is important for applications such as medical research, where it is necessary to share data without compromising the privacy of the patients. This paper surveys the state-of-the-art in privacy-preserving data fragmentation and aggregation techniques. We discuss the different challenges that need to be addressed in this area, and we present a number of different techniques that have been proposed. We al

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COSTEA, Ioan. "Data Privacy Assurance in Virtual Private Networks." International Journal of Information Security and Cybercrime 1, no. 2 (2012): 40–47. http://dx.doi.org/10.19107/ijisc.2012.02.05.

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Dissertations / Theses on the topic "And Data Privacy"

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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 : - 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). - Adapting aggregate data computation techniques to the Trusted Cells environment, with the example of Pri

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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 basi

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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 medica

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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.

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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

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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 i

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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

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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

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Ma, Jianjie. "Learning from perturbed data for privacy-preserving data mining." Online access for everyone, 2006. http://www.dissertations.wsu.edu/Dissertations/Summer2006/j%5Fma%5F080406.pdf.

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Books on the topic "And Data Privacy"

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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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South African Law Reform Commission. Privacy and data protection. South African Law Reform Commission], 2003.

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Book chapters on the topic "And Data Privacy"

1

Gardner, Anthony Luzzatto. "Data Privacy." In Stars with Stripes. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-29966-8_5.

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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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Fleckenstein, Mike, and Lorraine Fellows. "Data Privacy." In Modern Data Strategy. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-68993-7_14.

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Agrawal, Rakesh. "Data Privacy." In Machine Learning: ECML 2004. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-30115-8_2.

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Agrawal, Rakesh. "Data Privacy." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-30116-5_2.

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Bezzi, Michele, Sabrina De Capitani di Vimercati, Sara Foresti, Giovanni Livraga, Stefano Paraboschi, and Pierangela Samarati. "Data Privacy." In Privacy and Identity Management for Life. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-20317-6_8.

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Reid, Rob. "Data Privacy." In Practical CockroachDB. Apress, 2022. http://dx.doi.org/10.1007/978-1-4842-8224-3_6.

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Shukla, Samiksha, Jossy P. George, Kapil Tiwari, and Joseph Varghese Kureethara. "Data Privacy." In Data Ethics and Challenges. Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-0752-4_2.

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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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Conference papers on the topic "And Data Privacy"

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Gu, Yuechun, and Keke Chen. "Calibrating Practical Privacy Risks for Differentially Private Machine Learning." In 2024 IEEE International Conference on Big Data (BigData). IEEE, 2024. https://doi.org/10.1109/bigdata62323.2024.10825354.

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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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Srinivasan, S. "Privacy Protection and Data Breaches." In InSITE 2015: Informing Science + IT Education Conferences: USA. Informing Science Institute, 2015. http://dx.doi.org/10.28945/2261.

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Data breach is the act of accessing a central data repository without the consent of the data owner. Data breaches are occurring frequently and involve millions of records. Major breaches have been reported since 2005. Often data breaches occur due to someone with malicious intent accessing the stored data. In this paper we look at the types of data breaches and how they impact people’s privacy, we introduce a data protection model with the goal of protecting people’s privacy. Given today’s mobile information needs it is essential to have access to personal data. Social networks are making it

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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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Chaves, Iago, and Javam Machado. "Differentially Private Group-by Data Releasing Algorithm." In XXXIV Simpósio Brasileiro de Banco de Dados. Sociedade Brasileira de Computação - SBC, 2019. http://dx.doi.org/10.5753/sbbd.2019.8835.

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Privacy concerns are growing fast because of data protection regulations around the world. Many works have built private algorithms avoiding sensitive information leakage through data publication. Differential privacy, based on formal definitions, is a strong guarantee for individual privacy and the cutting edge for designing private algorithms. This work proposes a differentially private group-by algorithm for data publication under the exponential mechanism. Our method publishes data groups according to a specified attribute while maintaining the desired privacy level and trustworthy utility

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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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Huai, Mengdi, Di Wang, Chenglin Miao, Jinhui Xu, and Aidong Zhang. "Privacy-aware Synthesizing for Crowdsourced Data." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/353.

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Although releasing crowdsourced data brings many benefits to the data analyzers to conduct statistical analysis, it may violate crowd users' data privacy. A potential way to address this problem is to employ traditional differential privacy (DP) mechanisms and perturb the data with some noise before releasing them. However, considering that there usually exist conflicts among the crowdsourced data and these data are usually large in volume, directly using these mechanisms can not guarantee good utility in the setting of releasing crowdsourced data. To address this challenge, in this paper, we

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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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Reports on the topic "And Data Privacy"

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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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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 pr

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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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Lin, Shurong, and Eric Kolaczyk. Data Privacy for Record Linkage and Beyond. National Bureau of Economic Research, 2024. http://dx.doi.org/10.3386/w32940.

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Drechsler, Jörg, and James Bailie. The Complexities of Differential Privacy for Survey Data. National Bureau of Economic Research, 2024. http://dx.doi.org/10.3386/w32905.

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