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arXiv · 2507.06969

Unifying Re-Identification, Attribute Inference, and Data Reconstruction Risks in Differential Privacy

Bogdan Kulynych·Juan Felipe Gomez·Georgios Kaissis·Jamie Hayes·Borja Balle·Flavio P. Calmon·Jean Louis Raisaro
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Abstract

Differentially private (DP) mechanisms are difficult to interpret and calibrate because existing methods for mapping standard privacy parameters to concrete privacy risks -- re-identification, attribute inference, and data reconstruction -- are both overly pessimistic and inconsistent. In this work, we use the hypothesis-testing interpretation of DP ($f$-DP), and determine that bounds on attack success can take the same unified form across re-identification, attribute inference, and data reconstruction risks. Our unified bounds are (1) consistent across a multitude of attack settings, and (2) tunable, enabling practitioners to evaluate risk with respect to arbitrary, including worst-case, levels of baseline risk. Empirically, our results are tighter than prior methods using $\varepsilon$-DP, Rényi DP, and concentrated DP. As a result, calibrating noise using our bounds can reduce the required noise by 20% at the same risk level, which yields, e.g., an accuracy increase from 52% to 70% in a text classification task. Overall, this unifying perspective provides a principled framework for interpreting and calibrating the degree of protection in DP against specific levels of re-identification, attribute inference, or data reconstruction risk.

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Bogdan Kulynych, Juan Felipe Gomez, Georgios Kaissis, Jamie Hayes, Borja Balle, Flavio P. Calmon, Jean Louis Raisaro. 2026-02-04. Unifying Re-Identification, Attribute Inference, and Data Reconstruction Risks in Differential Privacy. https://arxiv.org/abs/2507.06969

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