mlsanityData to 14 Oct 2026

That which we call private

arXiv 1908.03566 ↗First posted 8 Aug 2019cs.LG cs.AI cs.CRcited by 16

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Abstract (arXiv)

The guarantees of security and privacy defenses are often strengthened by relaxing the assumptions made about attackers or the context in which defenses are deployed. Such relaxations can be a highly worthwhile topic of exploration---even though they typically entail assuming a weaker, less powerful adversary---because there may indeed be great variability in both attackers' powers and their context. However, no weakening or contextual discounting of attackers' power is assumed for what some have called "relaxed definitions" in the analysis of differential-privacy guarantees. Instead, the definitions so named are the basis of refinements and more advanced analyses of the worst-case implications of attackers---without any change assumed in attackers' powers. Because they more precisely bound the worst-case privacy loss, these improved analyses can greatly strengthen the differential-privacy upper-bound guarantees---sometimes lowering the differential-privacy epsilon by orders-of-magnitude. As such, to the casual eye, these analyses may appear to imply a reduced privacy loss. This is a false perception: the privacy loss of any concrete mechanism cannot change with the choice of a worst-case-loss upper-bound analysis technique. Practitioners must be careful not to equate real-world privacy with differential-privacy epsilon values, at least not without full consideration of the context.

Key facts. Cited by 16 papers in the map (data through 14 Oct 2026). First posted on arXiv 8 Aug 2019.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 4; 2022: 6; 2023: 2; 2024: 2; 2025: 0; 2026: 2.

16 papers.

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  1. 2021Enhanced Membership Inference Attacks against Machine Learning Modelscited by 248
  2. 2023How to DP-fy ML: A Practical Guide to Machine Learning with Differential Privacycited by 153
  3. 2021LinkTeller: Recovering Private Edges from Graph Neural Networks via Influence Analysiscited by 65
  4. 2022Improved Differential Privacy for SGD via Optimal Private Linear Operators on Adaptive Streamscited by 59
  5. 2021Antipodes of Label Differential Privacy: PATE and ALIBIcited by 52
  6. 2022SoK: Let the Privacy Games Begin! A Unified Treatment of Data Inference Privacy in Machine Learningcited by 51
  7. 2022Bounding Training Data Reconstruction in Private (Deep) Learningcited by 46
  8. 2022Bayesian Estimation of Differential Privacycited by 33
  9. 2024Membership Inference Attacks and Defenses in Federated Learning: A Surveycited by 31
  10. 2022Analyzing Privacy Leakage in Machine Learning via Multiple Hypothesis Testing: A Lesson From Fanocited by 17
  11. 2022Bounding Membership Inferencecited by 15
  12. 2024Attack-Aware Noise Calibration for Differential Privacycited by 8
4 more
  1. 2023Probing the Transition to Dataset-Level Privacy in ML Models Using an Output-Specific and Data-Resolved Privacy Profilecited by 3
  2. 2021D3p -- A Python Package for Differentially-Private Probabilistic Programmingcited by 2
  3. 2026Understanding Disclosure Risk in Differential Privacy with Applications to Noise Calibration and Auditing (Extended Version)cited by 2
  4. 2026On Privacy in Data-Space Tabular Diffusion Models: Influential Factors, Attacker Knowledge, and Metricscited by 0