Private Sampling with Identifiable Cheaters
Document type :
Article dans une revue scientifique
Title :
Private Sampling with Identifiable Cheaters
Author(s) :
Sabater, César [Auteur]
Machine Learning in Information Networks [MAGNET]
Hahn, Florian [Auteur]
University of Twente
Peter, Andreas [Auteur]
University of Oldenburg
Ramon, Jan [Auteur]
Machine Learning in Information Networks [MAGNET]
Machine Learning in Information Networks [MAGNET]
Hahn, Florian [Auteur]
University of Twente
Peter, Andreas [Auteur]
University of Oldenburg
Ramon, Jan [Auteur]

Machine Learning in Information Networks [MAGNET]
Journal title :
Proceedings on Privacy Enhancing Technologies
Publisher :
Privacy Enhancing Technologies Symposium
Publication date :
2023
ISSN :
2299-0984
English keyword(s) :
differential privacy
sampling
zero knowledge proofs
multiparty computation
sampling
zero knowledge proofs
multiparty computation
HAL domain(s) :
Informatique [cs]/Apprentissage [cs.LG]
English abstract : [en]
In this paper we study verifiable sampling from probability distributions in the context of multi-party computation. This has various applications in randomized algorithms performed collaboratively by parties not trusting ...
Show more >In this paper we study verifiable sampling from probability distributions in the context of multi-party computation. This has various applications in randomized algorithms performed collaboratively by parties not trusting each other. One example is differentially private machine learning where noise should be drawn, typically from a Laplace or Gaussian distribution, and it is desirable that no party can bias this process. In particular, we propose algorithms to draw random numbers from uniform, Laplace, Gaussian and arbitrary probability distributions, and to verify honest execution of the protocols through zero-knowledge proofs. We propose protocols that result in one party knowing the drawn number and protocols that deliver the drawn random number as a shared secret.Show less >
Show more >In this paper we study verifiable sampling from probability distributions in the context of multi-party computation. This has various applications in randomized algorithms performed collaboratively by parties not trusting each other. One example is differentially private machine learning where noise should be drawn, typically from a Laplace or Gaussian distribution, and it is desirable that no party can bias this process. In particular, we propose algorithms to draw random numbers from uniform, Laplace, Gaussian and arbitrary probability distributions, and to verify honest execution of the protocols through zero-knowledge proofs. We propose protocols that result in one party knowing the drawn number and protocols that deliver the drawn random number as a shared secret.Show less >
Language :
Anglais
Peer reviewed article :
Oui
Audience :
Internationale
Popular science :
Non
ANR Project :
Collections :
Source :
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