• Corpus ID: 14861086

Concentrated Differential Privacy

@article{Dwork2016ConcentratedDP,
  title={Concentrated Differential Privacy},
  author={Cynthia Dwork and Guy N. Rothblum},
  journal={ArXiv},
  year={2016},
  volume={abs/1603.01887}
}
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. 
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273 Citations
Highly Influential Citations
28
Background Citations
164
Methods Citations
54
Results Citations
7

273 Citations

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Stronger Privacy Amplification by Shuffling for Rényi and Approximate Differential Privacy

TLDR
The shuffle model of differential privacy has gained significant interest as an intermediate trust model between the standard local and central models and leads to tighter numerical bounds in all parameter settings.

Renyi Differential Privacy Mechanisms for Posterior Sampling

TLDR
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TLDR
This paper argues that the standard formalization of differential privacy is stricter than required by the intuitive privacy guarantee it seeks, and proposes individual differential privacy, an alternative differential privacy notion that offers the same privacy guarantees as standard differential privacy to individuals (even though not to groups of individuals).

Algorithms with More Granular Differential Privacy Guarantees

TLDR
This work considers partial differential privacy (DP), which allows quantifying the privacy guarantee on a per- attribute basis, and designs algorithms whose per-attribute privacy parameter is smaller that the best possible privacy parameter for the entire record of a person.

Information-theoretic metrics for Local Differential Privacy protocols

TLDR
New information-theoretic metrics for utility and privacy in local Differential Privacy protocols are introduced, showing how they relate to $\varepsilon$-LDP, the \emph{de facto} standard privacy metric, giving an information- theoretic interpretation to the latter.

Bayesian Differential Privacy for Machine Learning

TLDR
Bayesian differential privacy (BDP) is proposed, which takes into account the data distribution to provide more practical privacy guarantees and derives a general privacy accounting method under BDP, building upon the well-known moments accountant.
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