Private Sampling with Identifiable Cheaters

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

Type de document :

Article dans une revue scientifique

Titre :

Private Sampling with Identifiable Cheaters

Auteur(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]

Titre de la revue :

Proceedings on Privacy Enhancing Technologies

Éditeur :

Privacy Enhancing Technologies Symposium

Date de publication :

2023

ISSN :

2299-0984

Mot(s)-clé(s) en anglais :

differential privacy
sampling
zero knowledge proofs
multiparty computation

Discipline(s) HAL :

Informatique [cs]/Apprentissage [cs.LG]

Résumé en anglais : [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 ...
Lire la suite >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.Lire moins >

Langue :

Anglais

Comité de lecture :

Oui

Audience :

Internationale

Vulgarisation :

Non

Projet ANR :

Recherche en Medicine respectant la vie Privée

Collections :