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pro vyhledávání: '"Lebeda, Christian Janos"'
Autor:
Lebeda, Christian Janos
We present a simple variant of the Gaussian mechanism for answering differentially private queries when the sensitivity space has a certain common structure. Our motivating problem is the fundamental task of answering $d$ counting queries under the a
Externí odkaz:
http://arxiv.org/abs/2408.06853
We consider the problem of computing tight privacy guarantees for the composition of subsampled differentially private mechanisms. Recent algorithms can numerically compute the privacy parameters to arbitrary precision but must be carefully applied.
Externí odkaz:
http://arxiv.org/abs/2405.20769
Differentially private mean estimation is an important building block in privacy-preserving algorithms for data analysis and machine learning. Though the trade-off between privacy and utility is well understood in the worst case, many datasets exhibi
Externí odkaz:
http://arxiv.org/abs/2306.08745
Autor:
Lebeda, Christian Janos, Tětek, Jakub
We consider the problem of computing differentially private approximate histograms and heavy hitters in a stream of elements. In the non-private setting, this is often done using the sketch of Misra and Gries [Science of Computer Programming, 1982].
Externí odkaz:
http://arxiv.org/abs/2301.02457
Representing a sparse histogram, or more generally a sparse vector, is a fundamental task in differential privacy. An ideal solution would use space close to information-theoretical lower bounds, have an error distribution that depends optimally on t
Externí odkaz:
http://arxiv.org/abs/2106.10068
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