Private Sampling with Identifiable Cheaters

Sabater, César; Hahn, Florian; Peter, Andreas; Ramon, Jan

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]

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

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 >

Language :

Anglais

Peer reviewed article :

Oui

Audience :

Internationale

Popular science :

Non

ANR Project :

Recherche en Medicine respectant la vie Privée

Collections :