Most cited papers in Privacy-Preserving Technologies in Data
What are the most cited papers in Privacy-Preserving Technologies in Data?
The most cited paper in Privacy-Preserving Technologies in Data is "k-ANONYMITY: A MODEL FOR PROTECTING PRIVACY" (Latanya Sweeney, 2002), with 8,620 citations. Next come "Calibrating Noise to Sensitivity in Private Data Analysis" (7,243) and "Deep Learning with Differential Privacy" (6,312).
Showing 50 of 99 papers. Every paper with more than 1,171 citations is listed.
| # | Paper | ||
|---|---|---|---|
| 1 | k-ANONYMITY: A MODEL FOR PROTECTING PRIVACY Latanya Sweeney · International Journal of Uncertainty Fuzziness and Knowledge-Based Systems | Oct 2002 | 8,620 |
| 2 | Calibrating Noise to Sensitivity in Private Data Analysis Cynthia Dwork, Frank McSherry, Kobbi Nissim et al. · Lecture notes in computer science | 2006 | 7,243 |
| 3 | Deep Learning with Differential Privacy Martı́n Abadi, Andy Chu, Ian Goodfellow et al. | 24 Oct 2016 | 6,312 |
| 4 | Federated Machine Learning Qiang Yang, Yang Liu, Tianjian Chen et al. · ACM Transactions on Intelligent Systems and Technology | 28 Jan 2019 | 6,287 |
| 5 | Large-Scale Machine Learning with Stochastic Gradient Descent Léon Bottou | 2010 | 5,757 |
| 6 | Advances and Open Problems in Federated Learning Peter Kairouz et al. · Foundations and Trends® in Machine Learning | 2 Dec 2020 | 5,510 |
| 7 | Differential Privacy Cynthia Dwork · Lecture notes in computer science | 2006 | 5,365 |
| 8 | Communication-Efficient Learning of Deep Networks from Decentralized Data H. Brendan McMahan, Eider Moore, Daniel Ramage et al. · arXiv (Cornell University) | 17 Feb 2016 | 5,182 |
| 9 | Federated Learning: Challenges, Methods, and Future Directions Tian Li, Anit Kumar Sahu, Ameet Talwalkar et al. · IEEE Signal Processing Magazine | May 2020 | 5,060 |
| 10 | Attribute-based encryption for fine-grained access control of encrypted data Vipul Goyal, Omkant Pandey, Amit Sahai et al. | 30 Oct 2006 | 5,045 |
| 11 | Ciphertext-Policy Attribute-Based Encryption John Bethencourt, Amit Sahai, Brent R. Waters | May 2007 | 5,026 |
| 12 | The Algorithmic Foundations of Differential Privacy Cynthia Dwork, Aaron Roth · Foundations and Trends® in Theoretical Computer Science | 11 Aug 2014 | 4,662 |
| 13 | Membership Inference Attacks Against Machine Learning Models Reza Shokri, Marco Stronati, Congzheng Song et al. | May 2017 | 4,547 |
| 14 | Practical Secure Aggregation for Privacy-Preserving Machine Learning Keith Bonawitz, Vladimir Ivanov, Ben Kreuter et al. | 27 Oct 2017 | 3,808 |
| 15 | L -diversity Ashwin Machanavajjhala, Daniel Kifer, Johannes E. Gehrke et al. · ACM Transactions on Knowledge Discovery from Data | Mar 2007 | 3,639 |
| 16 | Journal of Statistical Software Jan de Leeuw · Wiley Interdisciplinary Reviews Computational Statistics | 30 Jun 2009 | 3,609 |
| 17 | Probabilistic Matrix Factorization Andriy Mnih, Ruslan Salakhutdinov · Neural Information Processing Systems | 3 Dec 2007 | 3,566 |
| 18 | On the importance of initialization and momentum in deep learning Ilya Sutskever, James Martens, George E. Dahl et al. | 16 Jun 2013 | 3,519 |
| 19 | Fairness through awareness Cynthia Dwork, Moritz Hardt, Toniann Pitassi et al. | 8 Jan 2012 | 3,494 |
| 20 | t-Closeness: Privacy Beyond k-Anonymity and l-Diversity Ninghui Li, Tiancheng Li, Suresh Venkatasubramanian | Apr 2007 | 3,408 |
| 21 | Differential Privacy: A Survey of Results Cynthia Dwork · Lecture notes in computer science | 29 Apr 2008 | 3,407 |
| 22 | Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy Tony Doyle · The Information Society | 3 Oct 2017 | 3,127 |
| 23 | Federated Learning: Strategies for Improving Communication Efficiency Jakub Konečný, H. Brendan McMahan, Felix X. Yu et al. · arXiv (Cornell University) | 18 Oct 2016 | 3,043 |
| 24 | The future of digital health with federated learning Nicola Rieke, Jonny Hancox, Wenqi Li et al. · npj Digital Medicine | 14 Sep 2020 | 3,041 |
| 25 | Privacy-preserving data mining Rakesh Agrawal, Ramakrishnan Srikant · ACM SIGMOD Record | 16 May 2000 | 2,990 |
| 26 | Model Inversion Attacks that Exploit Confidence Information and Basic Countermeasures Matt Fredrikson, Somesh Jha, Thomas Ristenpart | 6 Oct 2015 | 2,905 |
| 27 | Provable data possession at untrusted stores Giuseppe Ateniese, Randal C. Burns, Reza Curtmola et al. | 28 Oct 2007 | 2,717 |
| 28 | Efficient Multi-User Computation Offloading for Mobile-Edge Cloud Computing Xu Chen, Lei Jiao, Wenzhong Li et al. · IEEE/ACM Transactions on Networking | 26 Oct 2015 | 2,706 |
| 29 | An Extended Privacy Calculus Model for E-Commerce Transactions Tamara Dinev, Paul Hart · Information Systems Research | Mar 2006 | 2,634 |
| 30 | Decentralizing Privacy: Using Blockchain to Protect Personal Data Guy Zyskind, Oz Nathan, Alex Pentland | May 2015 | 2,577 |
| 31 | Federated Learning in Mobile Edge Networks: A Comprehensive Survey Wei Yang Bryan Lim, Nguyen Cong Luong, Dinh Thai Hoang et al. · IEEE Communications Surveys & Tutorials | 2020 | 2,561 |
| 32 | Completeness theorems for non-cryptographic fault-tolerant distributed computation Michael Ben-Or, Avi Wigderson | 1988 | 2,542 |
| 33 | MedRec: Using Blockchain for Medical Data Access and Permission Management Asaph Azaria, Ariel Ekblaw, Thiago Augusto Vieira et al. | Aug 2016 | 2,494 |
| 34 | A Theory for Record Linkage Ivan P. Fellegi, Alan B. Sunter · Journal of the American Statistical Association | Dec 1969 | 2,443 |
| 35 | Edge Intelligence: Paving the Last Mile of Artificial Intelligence With Edge Computing Zhi Zhou, Xu Chen, En Li et al. · Proceedings of the IEEE | 12 Jun 2019 | 2,398 |
| 36 | L-diversity: privacy beyond k-anonymity Ashwin Machanavajjhala, Johannes E. Gehrke, Daniel Kifer et al. | 2006 | 2,392 |
| 37 | Federated Learning With Differential Privacy: Algorithms and Performance Analysis Kang Wei, Jun Li, Ming Ding et al. · IEEE Transactions on Information Forensics and Security | 2020 | 2,379 |
| 38 | Robust De-anonymization of Large Sparse Datasets Arvind Narayanan, Vitaly Shmatikov · Proceedings - IEEE Symposium on Security and Privacy/Proceedings of the ... IEEE Symposium on Security and Privacy | May 2008 | 2,357 |
| 39 | Protecting respondents identities in microdata release Pierangela Samarati · IEEE Transactions on Knowledge and Data Engineering | 2001 | 2,337 |
| 40 | Privacy-Preserving Deep Learning Reza Shokri, Vitaly Shmatikov | 6 Oct 2015 | 2,329 |
| 41 | Adaptive Federated Learning in Resource Constrained Edge Computing Systems Shiqiang Wang, Tiffany Tuor, Theodoros Salonidis et al. · IEEE Journal on Selected Areas in Communications | 11 Mar 2019 | 2,302 |
| 42 | Datafication, dataism and dataveillance: Big Data between scientific paradigm and ideology José van Dijck · Surveillance & Society | 9 May 2014 | 2,190 |
| 43 | Distributed asynchronous deterministic and stochastic gradient optimization algorithms John N. Tsitsiklis, Dimitri P. Bertsekas, Michael Athans · IEEE Transactions on Automatic Control | Sep 1986 | 2,042 |
| 44 | Information revelation and privacy in online social networks Ralph Gross, Alessandro Acquisti | 7 Nov 2005 | 2,035 |
| 45 | Information Privacy Research: an Interdisciplinary Review1 Smith, Tamara Dinev, Xu · MIS Quarterly | Dec 2011 | 2,028 |
| 46 | ACHIEVING k-ANONYMITY PRIVACY PROTECTION USING GENERALIZATION AND SUPPRESSION Latanya Sweeney · International Journal of Uncertainty Fuzziness and Knowledge-Based Systems | Oct 2002 | 1,986 |
| 47 | Equality of Opportunity in Supervised Learning Moritz Hardt, Eric Price, Nathan Srebro · arXiv (Cornell University) | 7 Oct 2016 | 1,916 |
| 48 | Ciphertext-Policy Attribute-Based Encryption: An Expressive, Efficient, and Provably Secure Realization Brent R. Waters · Lecture notes in computer science | 2011 | 1,911 |
| 49 | SecureML: A System for Scalable Privacy-Preserving Machine Learning Payman Mohassel, Yupeng Zhang | May 2017 | 1,906 |
| 50 | Accelerating Stochastic Gradient Descent using Predictive Variance Reduction Rie Johnson, Tong Zhang | 5 Dec 2013 | 1,905 |
Citations are OpenAlex's own counts, from its 2026-09-23 snapshot, so the ranking can differ from Google Scholar or Web of Science. A paper is listed for a field when OpenAlex files it under one of the field's topics; retracted papers and papers whose citation count cannot be traced to citing works are left out, and a preprint and the article it became are shown once. Release dates are OpenAlex's: where a source gave only a month or a year, that is what is shown. How the lists are chosen.