Zobrazeno 1 - 10
of 149
pro vyhledávání: '"BEIMEL, AMOS"'
Autor:
Beimel, Amos, Kaplan, Haim, Mansour, Yishay, Nissim, Kobbi, Saranurak, Thatchaphol, Stemmer, Uri
A dynamic algorithm against an adaptive adversary is required to be correct when the adversary chooses the next update after seeing the previous outputs of the algorithm. We obtain faster dynamic algorithms against an adaptive adversary and separatio
Externí odkaz:
http://arxiv.org/abs/2111.03980
In his seminal work, Cleve [STOC '86] has proved that any $r$-round coin-flipping protocol can be efficiently biased by $\Theta(1/r)$. This lower bound was met for the two-party case by Moran, Naor, and Segev [Journal of Cryptology '16], and the thre
Externí odkaz:
http://arxiv.org/abs/2105.00743
The shuffle model of differential privacy was proposed as a viable model for performing distributed differentially private computations. Informally, the model consists of an untrusted analyzer that receives messages sent by participating parties via
Externí odkaz:
http://arxiv.org/abs/2009.13510
Let~$\cH$ be a class of boolean functions and consider a {\it composed class} $\cH'$ that is derived from~$\cH$ using some arbitrary aggregation rule (for example, $\cH'$ may be the class of all 3-wise majority-votes of functions in $\cH$). We upper
Externí odkaz:
http://arxiv.org/abs/2003.04509
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
Externí odkaz:
http://arxiv.org/abs/1912.08951
In a recent paper Chan et al. [SODA '19] proposed a relaxation of the notion of (full) memory obliviousness, which was introduced by Goldreich and Ostrovsky [J. ACM '96] and extensively researched by cryptographers. The new notion, differential obliv
Externí odkaz:
http://arxiv.org/abs/1905.01373
We present a private learner for halfspaces over an arbitrary finite domain $X\subset \mathbb{R}^d$ with sample complexity $mathrm{poly}(d,2^{\log^*|X|})$. The building block for this learner is a differentially private algorithm for locating an appr
Externí odkaz:
http://arxiv.org/abs/1902.10731
Autor:
Beimel, Amos, Brafman, Ronen I.
In privacy-preserving multi-agent planning, a group of agents attempt to cooperatively solve a multi-agent planning problem while maintaining private their data and actions. Although much work was carried out in this area in past years, its theoretic
Externí odkaz:
http://arxiv.org/abs/1810.13354