Privacy-Preserving Adversarial Networks
Open accessAbstract
We propose a data-driven framework for optimizing privacy-preserving data release mechanisms to attain the information-theoretically optimal tradeoff between minimizing distortion of useful data and concealing specific sensitive information. Our approach employs adversarially-trained neural networks to implement randomized mechanisms and to perform a variational approximation of mutual information privacy. We validate our Privacy-Preserving Adversarial Networks (PPAN) framework via proof-of-concept experiments on discrete and continuous synthetic data, as well as the MNIST handwritten digits dataset. For synthetic data, our model-agnostic PPAN approach achieves tradeoff points very close to the optimal tradeoffs that are analytically-derived from model knowledge. In experiments with the MNIST data, we visually demonstrate a learned tradeoff between minimizing the pixel-level distortion versus concealing the written digit.
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Examples of GAN-based defense models include generative adversarial privacy, privacy-preserving adversarial networks, compressive adversarial privacy, and reconstructive adversarial network.
“Examples of defense models include generative adversarial privacy [30], privacy-preserving adversarial networks [31], compressive adversarial privacy [32], and reconstructive adversarial network [33].”
From Generative AI in medical practice: in-depth exploration of privacy and security challenges (2024) · cites this paper as [31]
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Generating Fair Universal Representations using Adversarial Models
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Distributed Semi-Private Image Classification Based on Information-Bottleneck Principle
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- Authors
- Tripathy, A., Wang, Y., & Ishwar, P.
- Affiliations
- University of Wisconsin–Madison (US)
- Mitsubishi Electric (United States) (US)
- Boston University (US)
- Venue
- 2019 57th Annual Allerton Conference on Communication, Control, and Computing (Allerton)
- Publisher
- IEEE
- Published
- 2019-09
- Pages
- 495-505
- OpenAlex
- W2779622762
- Cited by
- 87
- References
- 37
- Citations by year
- 2026: 2 · 2025: 5 · 2024: 9 · 2023: 13 · 2022: 10 · 2021: 14 · 2020: 15 · 2019: 15 · 2018: 2 · 2017: 2
- FWCI
- 5.16
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- Allerton Conference on Communication, Control, and Computing (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Physical Sciences › Computer Science › Artificial Intelligence › Adversarial Robustness in Machine Learning
- Physical Sciences › Computer Science › Artificial Intelligence › Cryptography and Data Security
- Keywords
- privacy-preserving data publishing, differential privacy, adversarial neural networks, information-theoretic optimality, variational approximation, MNIST
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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