no code implementations • 28 Jan 2022 • Refael Kohen, Or Sheffet
The hybrid-model (Avent et al 2017) in Differential Privacy is a an augmentation of the local-model where in addition to N local-agents we are assisted by one special agent who is in fact a curator holding the sensitive details of n additional individuals.
no code implementations • 11 Jun 2020 • Kontantinos E. Nikolakakis, Dionysios S. Kalogerias, Or Sheffet, Anand D. Sarwate
First, we propose a (non-private) successive elimination algorithm for strictly optimal best-arm identification, we show that our algorithm is $\delta$-PAC and we characterize its sample complexity.
no code implementations • 18 Dec 2019 • Amos Beimel, Aleksandra Korolova, Kobbi Nissim, Or Sheffet, Uri Stemmer
Motivated by the desire to bridge the utility gap between local and trusted curator models of differential privacy for practical applications, we initiate the theoretical study of a hybrid model introduced by "Blender" [Avent et al.,\ USENIX Security '17], in which differentially private protocols of n agents that work in the local-model are assisted by a differentially private curator that has access to the data of m additional users.
no code implementations • NeurIPS 2019 • Gautam Kamath, Or Sheffet, Vikrant Singhal, Jonathan Ullman
Learning the parameters of Gaussian mixture models is a fundamental and widely studied problem with numerous applications.
no code implementations • 22 May 2019 • Touqir Sajed, Or Sheffet
We present a provably optimal differentially private algorithm for the stochastic multi-arm bandit problem, as opposed to the private analogue of the UCB-algorithm [Mishra and Thakurta, 2015; Tossou and Dimitrakakis, 2016] which doesn't meet the recently discovered lower-bound of $\Omega \left(\frac{K\log(T)}{\epsilon} \right)$ [Shariff and Sheffet, 2018].
no code implementations • NeurIPS 2018 • Roshan Shariff, Or Sheffet
We study the contextual linear bandit problem, a version of the standard stochastic multi-armed bandit (MAB) problem where a learner sequentially selects actions to maximize a reward which depends also on a user provided per-round context.
no code implementations • ICML 2018 • Or Sheffet
Under the mechanism of Bassily et al we give identity and independence testers with better sample complexity than the testers in the symmetric case, and we also propose a $\chi^2$-based identity tester which we investigate empirically.
no code implementations • ICML 2017 • Or Sheffet
Linear regression is one of the most prevalent techniques in machine learning, however, it is also common to use linear regression for its \emph{explanatory} capabilities rather than label prediction.
no code implementations • NeurIPS 2014 • Pranjal Awasthi, Avrim Blum, Or Sheffet, Aravindan Vijayaraghavan
We present the first polynomial time algorithm which provably learns the parameters of a mixture of two Mallows models.
no code implementations • 22 Aug 2012 • Jeremiah Blocki, Avrim Blum, Anupam Datta, Or Sheffet
Specifically, given a query f and a hypothesis H about the structure of a dataset D, we show generically how to transform f into a new query f_H whose global sensitivity (over all datasets including those that do not satisfy H) matches the restricted sensitivity of the query f. Moreover, if the belief of the querier is correct (i. e., D is in H) then f_H(D) = f(D).
Cryptography and Security Social and Information Networks Physics and Society