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The limits of differential privacy (and its misuse in data release and machine learning)

  • Published:2021-07 Issue:7 Volume:64 Page:33-35
  • ISSN:0001-0782
  • Container-title:Communications of the ACM
  • language:en
  • Short-container-title:Commun. ACM

Author:

Domingo-Ferrer Josep1,Sánchez David1,Blanco-Justicia Alberto1

Affiliation:

1. Universitat Rovira i Virgili, Tarragona, Catalonia

Abstract

Differential privacy is not a silver bullet for all privacy problems.

Publisher

Association for Computing Machinery (ACM)

Subject

General Computer Science

Reference15 articles.

1. An illustrated critique of differential privacy;Bambauer J.;Vanderbilt Journal of Entertainment & Technology Law

2. On syntactic anonymity and differential privacy;Clifton C.;Transactions on Data Privacy,2013

3. The Algorithmic Foundations of Differential Privacy;Dwork C.;Foundations and Trends in Theoretical Computer Science,2014

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1. Challenges of Using Synthetic Data Generation Methods for Tabular Microdata;Applied Sciences;2024-07-09

2. Benchmarking robustness and privacy-preserving methods in federated learning;Future Generation Computer Systems;2024-06

3. Meta-DPSTL: meta learning-based differentially private self-taught learning;International Journal of Machine Learning and Cybernetics;2024-05-09

4. When Privacy Protection Goes Wrong: How and Why the 2020 Census Confidentiality Program Failed;Journal of Economic Perspectives;2024-05-01

5. DeTA: Minimizing Data Leaks in Federated Learning via Decentralized and Trustworthy Aggregation;Proceedings of the Nineteenth European Conference on Computer Systems;2024-04-22
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