Zobrazeno 1 - 10
of 86
pro vyhledávání: '"Benny Pinkas"'
Autor:
Yehuda Lindell, Benny Pinkas
Publikováno v:
The Journal of Privacy and Confidentiality, Vol 1, Iss 1 (2009)
In this paper, we survey the basic paradigms and notions of secure multiparty computation and discuss their relevance to the field of privacy-preserving data mining. In addition to reviewing definitions and constructions for secure multiparty computa
Externí odkaz:
https://doaj.org/article/6bd44e22a4b7440aa6ea58f49c4064ce
Autor:
Daniel Günther, Marco Holz, Benjamin Judkewitz, Helen Möllering, Benny Pinkas, Thomas Schneider, Ajith Suresh
Publikováno v:
Proceedings of the 2022 ACM SIGSAC Conference on Computer and Communications Security.
Autor:
Gilad Asharov, Koki Hamada, Dai Ikarashi, Ryo Kikuchi, Ariel Nof, Benny Pinkas, Katsumi Takahashi, Junichi Tomida
Publikováno v:
Proceedings of the 2022 ACM SIGSAC Conference on Computer and Communications Security.
Publikováno v:
CCS
We present a highly-scalable secure computation of graph algorithms, which hides all information about the topology of the graph or other input values associated with nodes or edges. The setting is where all nodes and edges of the graph are secret-sh
Publikováno v:
Segal, S, Adi, Y, Pinkas, B, Baum, C, Ganesh, C & Keshet, J 2021, Fairness in the Eyes of the Data: Certifying Machine-Learning Models . in AIES 2021-Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society . Association for Computing Machinery, pp. 926-935, AAAI / ACM Conference on Artificial Intelligence, Ethics, and Society-AIES 2021, Virtual, 19/05/2021 . https://doi.org/10.1145/3461702.3462554
AIES
AIES
We present a framework that allows to certify the fairness degree of a model based on an interactive and privacy-preserving test. The framework verifies any trained model, regardless of its training process and architecture. Thus, it allows us to eva
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c3e7ffe8e04b677372ed52f5aee49d23
https://pure.au.dk/portal/da/publications/fairness-in-the-eyes-of-the-data-certifying-machinelearning-models(9436f94f-72a9-4016-8caf-c1b47d0d14e0).html
https://pure.au.dk/portal/da/publications/fairness-in-the-eyes-of-the-data-certifying-machinelearning-models(9436f94f-72a9-4016-8caf-c1b47d0d14e0).html
Autor:
Eyal Ronen, Benny Pinkas
Publikováno v:
Proceedings 2021 Innovative Secure IT Technologies against COVID-19 Workshop.
Autor:
Benny Pinkas
Publikováno v:
Proceedings of the 2020 ACM SIGSAC Conference on Computer and Communications Security.
Publikováno v:
CCS
Anonymous Committed Broadcast is a functionality that extends DC-nets and allows a set of clients to privately commit messages to set of servers, which can then simultaneously open all committed messages in a random ordering. Anonymity holds since no
Publikováno v:
IEEE Symposium on Security and Privacy
The IPv6 protocol was designed with security in mind. One of the changes that IPv6 has introduced over IPv4 is a new 20-bit flow label field in its protocol header.We show that remote servers can use the flow label field in order to assign a unique I
Autor:
Ittai Abraham, Yiming Zheng, Robert Chen, Guy Golan Gueta, Srinivas Devadas, Benny Pinkas, Alin Tomescu
Publikováno v:
IEEE Symposium on Security and Privacy
arXiv
arXiv
The resurging interest in Byzantine fault tolerant systems will demand more scalable threshold cryptosystems. Unfortunately, current systems scale poorly, requiring time quadratic in the number of participants. In this paper, we present techniques th