The Limits of Differential Privacy (and its Misuse in Data Release and Machine Learning)

4 November 2020

Papers citing "The Limits of Differential Privacy (and its Misuse in Data Release and Machine Learning)"

37 / 37 papers shown

Title
Evaluating Differentially Private Synthetic Data Generation in High-Stakes Domains Krithika Ramesh Nupoor Gandhi Pulkit Madaan Lisa Bauer Charith Peris Anjalie Field SyDa 40 2 0 10 Oct 2024
Sharper Bounds for Chebyshev Moment Matching with Applications to Differential Privacy and Beyond Cameron Musco Christopher Musco Lucas Rosenblatt A. Singh FedML 47 1 0 22 Aug 2024
Reconstructing training data from document understanding models Jérémie Dentan Arnaud Paran A. Shabou AAML SyDa 49 1 0 05 Jun 2024
Words Blending Boxes. Obfuscating Queries in Information Retrieval using Differential Privacy Francesco Luigi De Faveri G. Faggioli Nicola Ferro AAML 36 0 0 15 May 2024
Conciliating Privacy and Utility in Data Releases via Individual Differential Privacy and Microaggregation Jordi Soria-Comas David Sánchez J. Domingo-Ferrer Sergio Martínez Luis Del Vasto-Terrientes 19 0 0 21 Dec 2023
An Examination of the Alleged Privacy Threats of Confidence-Ranked Reconstruction of Census Microdata David Sánchez N. Jebreel J. Domingo-Ferrer K. Muralidhar Alberto Blanco-Justicia 16 1 0 06 Nov 2023
Statistical properties and privacy guarantees of an original distance-based fully synthetic data generation method Rémy Chapelle Bruno Falissard 35 0 0 10 Oct 2023
A Survey of Data Security: Practices from Cybersecurity and Challenges of Machine Learning Padmaksha Roy Jaganmohan Chandrasekaran Erin Lanus Laura J. Freeman Jeremy Werner 30 3 0 06 Oct 2023
Unraveling the Interconnected Axes of Heterogeneity in Machine Learning for Democratic and Inclusive Advancements Maryam Molamohammadi Afaf Taik Nicolas Le Roux G. Farnadi 29 1 0 11 Jun 2023
Patchwork Learning: A Paradigm Towards Integrative Analysis across Diverse Biomedical Data Sources Suraj Rajendran Weishen Pan M. Sabuncu Yong Chen Jiayu Zhou Fei Wang 57 14 0 10 May 2023
When Evolutionary Computation Meets Privacy Bowen Zhao Wei Chen Xiaoguo Li Ximeng Liu Qingqi Pei Jun Zhang 16 3 0 22 Mar 2023
Blockchain-Empowered Trustworthy Data Sharing: Fundamentals, Applications, and Challenges Linh-TX Nguyen L. Nguyen Thong Hoang Dilum Bandara Qin Wang Qinghua Lu Xiwei Xu Liming Zhu P. Popovski Shiping Chen 37 14 0 12 Mar 2023
Algorithmically Effective Differentially Private Synthetic Data Yi He Roman Vershynin Yizhe Zhu SyDa 30 8 0 11 Feb 2023
Database Reconstruction Is Not So Easy and Is Different from Reidentification K. Muralidhar J. Domingo-Ferrer 19 17 0 24 Jan 2023
BDSP: A Fair Blockchain-enabled Framework for Privacy-Enhanced Enterprise Data Sharing L. Nguyen James Hoang Qin Wang Qinghua Lu Sherry Xu Shiping Chen FedML 24 4 0 16 Dec 2022
Lessons Learned: Surveying the Practicality of Differential Privacy in the Industry Gonzalo Munilla Garrido Xiaoyuan Liu Florian Matthes D. Song 28 24 0 07 Nov 2022
GRAIMATTER Green Paper: Recommendations for disclosure control of trained Machine Learning (ML) models from Trusted Research Environments (TREs) E. Jefferson J. Liley Maeve Malone S. Reel Alba Crespi-Boixader ... Christian Cole F. Ritchie A. Daly Simon Rogers Jim Q. Smith 26 7 0 03 Nov 2022
On the Impossible Safety of Large AI Models El-Mahdi El-Mhamdi Sadegh Farhadkhani R. Guerraoui Nirupam Gupta L. Hoang Rafael Pinot Sébastien Rouault John Stephan 30 31 0 30 Sep 2022
"Am I Private and If So, how Many?" - Communicating Privacy Guarantees of Differential Privacy with Risk Communication Formats Daniel Franzen Saskia Nuñez von Voigt Peter Sorries Florian Tschorsch Claudia Muller-Birn 28 19 0 23 Aug 2022
Enhanced Security and Privacy via Fragmented Federated Learning N. Jebreel J. Domingo-Ferrer Alberto Blanco-Justicia David Sánchez FedML 33 26 0 13 Jul 2022
Conflicting Interactions Among Protection Mechanisms for Machine Learning Models S. Szyller Nadarajah Asokan AAML 26 7 0 05 Jul 2022
A Roadmap for Greater Public Use of Privacy-Sensitive Government Data: Workshop Report Chris Clifton B. Malin A. Oganian Ramesh Raskar Vivek Sharma FedML 11 0 0 17 Jun 2022
A Critical Review on the Use (and Misuse) of Differential Privacy in Machine Learning Alberto Blanco-Justicia David Sánchez J. Domingo-Ferrer K. Muralidhar 11 59 0 09 Jun 2022
Statistical Data Privacy: A Song of Privacy and Utility Aleksandra B. Slavkovic Jeremy Seeman 23 26 0 06 May 2022
Private measures, random walks, and synthetic data M. Boedihardjo Thomas Strohmer Roman Vershynin 15 0 0 20 Apr 2022
"Am I Private and If So, how Many?" -- Using Risk Communication Formats for Making Differential Privacy Understandable Daniel Franzen Saskia Nuñez von Voigt Peter Sorries Florian Tschorsch Claudia Muller-Birn Freie Universitat Berlin 31 9 0 08 Apr 2022
Privacy: An axiomatic approach Alexander Ziller Tamara T. Mueller R. Braren Daniel Rueckert Georgios Kaissis 9 2 0 22 Mar 2022
A review of Generative Adversarial Networks for Electronic Health Records: applications, evaluation measures and data sources Ghadeer O. Ghosheh Jin Li T. Zhu 35 32 0 14 Mar 2022
Statistical anonymity: Quantifying reidentification risks without reidentifying users Gecia Bravo Hermsdorff R. Busa-Fekete L. Gunderson Andrés Munoz Medina Umar Syed 8 1 0 28 Jan 2022
Fairness, Integrity, and Privacy in a Scalable Blockchain-based Federated Learning System Timon Rückel Johannes Sedlmeir Peter Hofmann FedML 19 58 0 11 Nov 2021
Bit-efficient Numerical Aggregation and Stronger Privacy for Trust in Federated Analytics Graham Cormode I. Markov FedML 19 10 0 03 Aug 2021
Revealing the Landscape of Privacy-Enhancing Technologies in the Context of Data Markets for the IoT: A Systematic Literature Review Gonzalo Munilla Garrido Johannes Sedlmeir Ömer Uludağ Ilias Soto Alaoui André Luckow Florian Matthes 22 46 0 25 Jul 2021
Dynamically Adjusting Case Reporting Policy to Maximize Privacy and Utility in the Face of a Pandemic J. T. Brown Chao Yan Weiyi Xia Zhijun Yin Zhiyu Wan A. Gkoulalas-Divanis Murat Kantarcioglu B. Malin 11 2 0 21 Jun 2021
Differential Privacy for Government Agencies -- Are We There Yet? Joerg Drechsler 26 20 0 17 Feb 2021
$k$ -Anonymity in Practice: How Generalisation and Suppression Affect Machine Learning Classifiers D. Slijepcevic Maximilian Henzl Lukas Daniel Klausner Tobias Dam Peter Kieseberg Matthias Zeppelzauer 8 40 0 09 Feb 2021
Achieving Security and Privacy in Federated Learning Systems: Survey, Research Challenges and Future Directions Alberto Blanco-Justicia J. Domingo-Ferrer Sergio Martínez David Sánchez Adrian Flanagan K. E. Tan FedML 26 110 0 12 Dec 2020
Graph Neural Networks in Recommender Systems: A Survey Shiwen Wu Fei Sun Wentao Zhang Xu Xie Bin Cui GNN 56 1,175 0 04 Nov 2020