Zobrazeno 1 - 10
of 155
pro vyhledávání: '"Salil Vadhan"'
Autor:
Merveille Koissi Savi, Akash Yadav, Wanrong Zhang, Navin Vembar, Andrew Schroeder, Satchit Balsari, Caroline O Buckee, Salil Vadhan, Nishant Kishore
Publikováno v:
PLOS Digital Health, Vol 2, Iss 10, p e0000233 (2023)
During the COVID-19 pandemic, the use of mobile phone data for monitoring human mobility patterns has become increasingly common, both to study the impact of travel restrictions on population movement and epidemiological modeling. Despite the importa
Externí odkaz:
https://doaj.org/article/fc00bf170c5d45f4be26142501ffe203
Autor:
Victor Balcer, Salil Vadhan
Publikováno v:
The Journal of Privacy and Confidentiality, Vol 9, Iss 2 (2019)
We consider the problem of designing and analyzing differentially private algorithms that can be implemented on discrete models of computation in strict polynomial time, motivated by known attacks on floating point implementations of real-arithmetic
Externí odkaz:
https://doaj.org/article/ff80f289751a4caea86e45985703ad32
Publikováno v:
Oberwolfach Reports. 18:2955-3005
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::66b9d56ed51e4cf98d5741e26841d71a
https://doi.org/10.4171/owr/2021/54
https://doi.org/10.4171/owr/2021/54
Publikováno v:
Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems.
Across academia, government, and industry, data stewards are facing increasing pressure to make datasets more openly accessible for researchers while also protecting the privacy of data subjects. Differential privacy (DP) is one promising way to offe
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7654a6dba9ea23bc0a7ca973c46ac471
https://doi.org/10.1145/3544548.3580791
https://doi.org/10.1145/3544548.3580791
Publikováno v:
Harvard Data Science Review.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::498ee1d66d98cdf1a60bd6e16011ad14
https://doi.org/10.1162/99608f92.cb06b469
https://doi.org/10.1162/99608f92.cb06b469
Publikováno v:
Theory of Computing. 17:1-35
We give a deterministic, nearly logarithmic-space algorithm that given an undirected graph $G$, a positive integer $r$, and a set $S$ of vertices, approximates the conductance of $S$ in the $r$-step random walk on $G$ to within a factor of $1+\epsilo
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::5b2086d1a503e6abfc5a9ea7239d2abe
https://doi.org/10.4086/toc.2021.v017a004
https://doi.org/10.4086/toc.2021.v017a004
Autor:
Jonathan Ullman, Salil Vadhan
Publikováno v:
Journal of Cryptology. 33:2078-2112
Assuming the existence of one-way functions, we show that there is no polynomial-time differentially private algorithm $${\mathcal {A}}$$ that takes a database $$D\in (\{0,1\}^d)^n$$ and outputs a “synthetic database” $${\hat{D}}$$ all of whose t
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d0122018e72a0305275b58702ace4898
https://doi.org/10.1007/s00145-020-09363-y
https://doi.org/10.1007/s00145-020-09363-y
Publikováno v:
Theory of Computing. 16:1-55
This paper uses a variant of the notion of \emph{inaccessible entropy} (Haitner, Reingold, Vadhan and Wee, STOC 2009), to give an alternative construction and proof for the fundamental result, first proved by Rompel (STOC 1990), that \emph{Universal
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a3bc6cfeebf5e95afbfc99b8343f249d
https://doi.org/10.4086/toc.2020.v016a008
https://doi.org/10.4086/toc.2020.v016a008
We identify a new class of vulnerabilities in implementations of differential privacy. Specifically, they arise when computing basic statistics such as sums, thanks to discrepancies between the implemented arithmetic using finite data types (namely,
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::096dfbd99d02771ed909048220b187d0
