ML Privacy Meter: Aiding Regulatory Compliance by Quantifying the Privacy Risks of Machine Learning

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Privacy-preserving deep learning
R. Shokri and V. Shmatikov · 2015
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Communication-efficient learning of deep networks from decentralized data
B. McMahan, E. Moore, D. Ramage, S. Hampson, and B. A. y Arcas · 2017
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Membership inference attacks against machine learning models
R. Shokri, M. Stronati, C. Song, and V. Shmatikov · 2017
Earlier work this paper cites.

Machine learning models that remember too much
C. Song, T. Ristenpart, and V. Shmatikov · 2017
Cited alongside, same era.
https://nvlpubs.nist.gov/nistpubs/ir/2019/NIST.IR.8269-draft.pdf
A taxonomy and terminology of adversarial machine learning · 2019
Cited alongside, same era.
The secret sharer: Evaluating and testing unintended memorization in neural networks
N. Carlini, C. Liu, Ú. Erlingsson, J. Kos, and D. Song · 2019
Cited alongside, same era.
https://ec.europa.eu/info/sites/info/files/commission-white-paper-artificial-intelligence-feb2020_en.pdf
On artificial intelligence - a european approach to excellence and trust
Cited in the paper.

Comprehensive privacy analysis of deep learning: Passive and active white-box inference attacks against centralized and federated learning
M. Nasr, R. Shokri, and A. Houmansadr · 2019
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https://ico.org.uk/media/about-the-ico/consultations/2617219/guidance-on-the-ai-auditing-framework-draft-for-consultation.pdf
Guidance on the ai auditing framework draft guidance for consultation. information commissioner’s office (2020) · 2020
Closest in time.
https://www.whitehouse.gov/wp-content/uploads/2020/01/Draft-OMB-Memo-on-Regulation-of-AI-1-7-19.pdf
A taxonomy and terminology of adversarial machine learning · 2020
Closest in time.