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We design an efficient method for sampling a large batch of dd independent coins with a given bias p∈[0,1]p∈[0,1]. The folklore secure computation method for doing so requires O(λ+logd)O(λ+log⁡d...
We consider the problem of designing scalable, robust protocols for computing statistics about sensitive data. Specifically, we look at how best to design differentially private protocols in a distrib...
In recent years, privacy enhancing technologies have gained tremendous momentum and they are expected to keep a sustained importance. Quantifying the degree of privacy offered by any mechanism working...
The problem of privatizing statistical databases is a well-studied topic that has culminated with the notion of differential privacy. The complementary problem of securing these databases, however, ha...
This technical report discusses three subtleties related to the widely used notion of differential privacy (DP). First, we discuss how the choice of a distinguisher influences the privacy notion and w...
Finally, we can capture impossibility results for differential privacy from risky traitor tracing. Since our ciphertexts are short (O(λ)O(λ)), thus we get the negative result which matches what one wo...
A central challenge in differential privacy is to design computationally efficient noninteractive algorithms that can answer large numbers of statistical queries on a sensitive dataset. That is, we wo...
The robustness of (approximate) differential privacy (DP) guarantees in the presence of thousands and even hundreds of thousands observations is crucial for many realistic application scenarios, such ...
"Concentrated differential privacy" was recently introduced by Dwork and Rothblum as a relaxation of differential privacy, which permits sharper analyses of many privacy-preserving computations. We pr...
Differential privacy is a mathematical definition of privacy for statistical data analysis. It guarantees that any (possibly adversarial) data analyst is unable to learn too much information that is...
In the design of differentially private mechanisms, it’s usually assumed that a uniformly random source is available. However, in many situations it seems unrealistic, and one must deal with various...
We consider how to perform privacy-preserving analyses on private data from different data providers and containing personal information of many different individuals. We combine differential privac...
In this paper, we introduce the notion of (, δ)-differential privacy in distribution, a strong version of the existing (, δ)-differential privacy, used to mathematically ensure that private data o...
In the study of differential privacy, composition theorems (starting with the original paper of Dwork, McSherry, Nissim, and Smith (TCC’06)) bound the degradation of privacy when composing several d...
This paper initiates the study of preserving {\em differential privacy} ({\sf DP}) when the data-set is sparse. We study the problem of constructing efficient sanitizer that preserves {\sf DP} and gua...

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