Privacy-preserving federated data sharing

Ferdinando Fioretto, Pascal Van Hentenryck

Research output: Chapter in Book/Entry/PoemConference contribution

12 Scopus citations

Abstract

Consider a set of agents with sensitive datasets who are interested in the same prediction task and would like to share their datasets without revealing private information For instance, the agents may be medical centers with their own historical databases and the task may be the diagnosis of a rare form of a disease This paper investigates whether sharing privacy-preserving versions of these datasets may improve the agent predictions It proposes a Privacy-preserving Federated Data Sharing (PFDS) protocol that each agent can run locally to produce a privacy-preserving version of its original dataset The PFDS protocol is evaluated on several standard prediction tasks and experimental results demonstrate the potential of sharing privacy-preserving datasets to produce accurate predictors.

Original languageEnglish (US)
Title of host publication18th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2019
PublisherInternational Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS)
Pages638-646
Number of pages9
ISBN (Electronic)9781510892002
StatePublished - 2019
Externally publishedYes
Event18th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2019 - Montreal, Canada
Duration: May 13 2019May 17 2019

Publication series

NameProceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS
Volume2
ISSN (Print)1548-8403
ISSN (Electronic)1558-2914

Conference

Conference18th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2019
Country/TerritoryCanada
CityMontreal
Period5/13/195/17/19

ASJC Scopus subject areas

  • Artificial Intelligence
  • Software
  • Control and Systems Engineering

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