A differential privacy mechanism with network effects for crowdsourcing systems
File(s)p1998.pdf (805.51 KB)
Published version
Author(s)
Luo, Y
Jennings, NR
Type
Conference Paper
Abstract
In crowdsourcing systems, it is important for the crowdsource campaign initiator to incentivize users to share their data to produce results of the desired computational accuracy. This problem becomes especially challenging when users are concerned about the privacy of their data. To overcome this challenge, existing work often aims to provide users with differential privacy guarantees to incentivize privacy-sensitive users to share their data. However, this work neglects the network effect that a user enjoys greater privacy protection when he aligns his participation behaviour with that of other users. To explore the network effect and provide a suitable differential privacy guarantee, we design PINE (Privacy Incentivization with Network Effects). PLNE is a mechanism that maximizes the initiator's payoff while providing participating users with privacy protections.
Date Issued
2018-07-15
Date Acceptance
2018-07-10
Citation
Proceedings of the 17th International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS, 2018, pp.1998-2000
ISBN
9781450356497
ISSN
2523-5699
Publisher
International Foundation for Autonomous Agents and MultiAgent Systems (IFAAMAS)
Start Page
1998
End Page
2000
Journal / Book Title
Proceedings of the 17th International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS
Copyright Statement
© 2018 by International Foundation for Autonomous Agents and MultiAgent Systems (IFAAMAS). All rights reserved.
Identifier
http://ifaamas.org/Proceedings/aamas2018/
Source
International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS 2018
Publication Status
Published
Start Date
2018-07-10
Finish Date
2018-07-15
Coverage Spatial
Stockholm, Sweden