A differential privacy mechanism with network effects for crowdsourcing systems

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Title: A differential privacy mechanism with network effects for crowdsourcing systems
Authors: Luo, Y
Jennings, NR
Item 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.
Issue Date: 15-Jul-2018
Date of Acceptance: 10-Jul-2018
URI: http://hdl.handle.net/10044/1/64362
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.
Conference Name: International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS 2018
Publication Status: Published
Start Date: 2018-07-10
Finish Date: 2018-07-15
Conference Place: Stockholm, Sweden
Appears in Collections:Faculty of Engineering
Computing
Faculty of Natural Sciences



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