Negotiation for Incentive Driven Privacy-Preserving Information Sharing

Reyhan Aydogan, Pinar Øzturk, Yousef Razeghi

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

3 Citations (Scopus)

Abstract

This paper describes an agent-based, incentive-driven, and privacy-preserving information sharing framework. Main contribution of the paper is to give the data provider agent an active role in the information sharing process and to change the currently asymmetric position between the provider and the requester of data and information (DI) to the favor of the DI provider. Instead of a binary yes/no answer to the requester’s data request and the incentive offer, the provider may negotiate about excluding from the requested DI bundle certain pieces of DI with high privacy value, and/or ask for a different type of incentive. We show the presented approach on a use case. However, the proposed architecture is domain independent.
Original languageEnglish
Title of host publication PRIMA 2017
Subtitle of host publicationPrinciples and Practice of Multi-Agent Systems - 20th International Conference - Proceedings
EditorsBo An, Ana Bazzan, João Leite, Serena Villata, Leendert van der Torre
Place of PublicationCham
PublisherSpringer
Pages486-494
Number of pages9
ISBN (Print)978-3-319-69130-5
DOIs
Publication statusPublished - 5 Oct 2017
EventPRIMA 2017: 20th International Conference on Principles and Practice of Multi-Agent Systems - Nice, France
Duration: 30 Oct 20173 Nov 2017
Conference number: 20
https://prima2017.gforge.uni.lu/

Publication series

NameLecture Notes in Computer Science
Volume10621
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

ConferencePRIMA 2017
CountryFrance
CityNice
Period30/10/173/11/17
Internet address

Keywords

  • Data and information sharing
  • Incentive-driven
  • Secrecy and privacy risk
  • Negotiation
  • Privacy-preserving agent systems

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