Library

Concentrated Differential Privacy

We introduce Concentrated Differential Privacy, a relaxation of Differential Privacy enjoying better accuracy than both pure differential privacy and its popular "(epsilon,delta)" relaxation without compromising on cumulative privacy loss over multiple computations.

cs.DScs.CRComputer ScienceMathematics
Source

Paper

Similar papers

Concentrated Differential Privacy: Simplifications, Extensions, and Lower BoundsMark Bun, T. Steinke2016 · Theory of Cryptography Conference · 1.0k citations In Library
Differential PrivacyXun Yi, Xuechao Yang, Xiaoning Liu et al.2025 In Library
Differential privacy: getting more for lessCynthia Dwork2023 · EMS Press eBooks In Library
Conservative or liberal? Personalized differential privacyZach Jorgensen, Ting Yu, Graham Cormode2015 · IEEE International Conference on Data Engineering · 265 citations In Library
Distribution-invariant differential privacyXuan Bi, Xiaotong Shen2021 · Journal of Econometrics · 24 citations In Library
Distribution-Invariant Differential PrivacyXuan Bi, Xiaotong Shen2021 · Journal of Econometrics · 24 citations In Library
Capacity Bounded Differential PrivacyKamalika Chaudhuri, Jacob Imola, Ashwin Machanavajjhala2019 · Neural Information Processing Systems · 41 citations In Library
Gaussian Differential PrivacyJinshuo Dong, Aaron Roth, Weijie Su2022 · Journal of the Royal Statistical Society Series B (Statistical Methodology) In Library
Bounding, Concentrating, and Truncating: Unifying Privacy Loss Composition for Data AnalyticsMark Cesar, Ryan Rogers2020 · arXiv (Cornell University) In Library
Bounding, Concentrating, and Truncating: Unifying Privacy Loss Composition for Data AnalyticsMark Cesar, Ryan Rogers2021 · Algorithmic Learning Theory In Library
© 2026 NYSGPT2525 LLC