Understanding mean-field effects of large-population user data obfuscation in machine learning

Alex Dunyak, Quanyan Zhu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Recently, data-based services have bloomed, because of the prevalence of online tracking, wearable computing, and the Internet of Things. However, due to privacy concerns, users may use tools to obfuscate their data, rendering these services less useful. This conflict places the service's desire for accuracy, and the users' desire for both accuracy and privacy, in contention. We propose a game-theoretic model for this conflict. By promising bounds on how much data gets collected and sold, services can incentivize users to report their data truthfully. We model this incentivization process as a Stackelberg game with the service provider as the leader. The users react to privacy promises as Stackelberg followers and interact with each other by playing a mean-field game. By representing the users as an interval on the continuum, we reduce the computational complexity of finding Nash and Stackelberg equilibriums.

Original languageEnglish (US)
Title of host publication2018 52nd Annual Conference on Information Sciences and Systems, CISS 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1-6
Number of pages6
ISBN (Electronic)9781538605790
DOIs
StatePublished - May 21 2018
Event52nd Annual Conference on Information Sciences and Systems, CISS 2018 - Princeton, United States
Duration: Mar 21 2018Mar 23 2018

Other

Other52nd Annual Conference on Information Sciences and Systems, CISS 2018
CountryUnited States
CityPrinceton
Period3/21/183/23/18

Fingerprint

Learning systems
Computational complexity
Internet of things

Keywords

  • Machine Learning
  • Mean-Field Game
  • Obfuscation
  • Stackelberg Game

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Information Systems

Cite this

Dunyak, A., & Zhu, Q. (2018). Understanding mean-field effects of large-population user data obfuscation in machine learning. In 2018 52nd Annual Conference on Information Sciences and Systems, CISS 2018 (pp. 1-6). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CISS.2018.8362267

Understanding mean-field effects of large-population user data obfuscation in machine learning. / Dunyak, Alex; Zhu, Quanyan.

2018 52nd Annual Conference on Information Sciences and Systems, CISS 2018. Institute of Electrical and Electronics Engineers Inc., 2018. p. 1-6.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Dunyak, A & Zhu, Q 2018, Understanding mean-field effects of large-population user data obfuscation in machine learning. in 2018 52nd Annual Conference on Information Sciences and Systems, CISS 2018. Institute of Electrical and Electronics Engineers Inc., pp. 1-6, 52nd Annual Conference on Information Sciences and Systems, CISS 2018, Princeton, United States, 3/21/18. https://doi.org/10.1109/CISS.2018.8362267
Dunyak A, Zhu Q. Understanding mean-field effects of large-population user data obfuscation in machine learning. In 2018 52nd Annual Conference on Information Sciences and Systems, CISS 2018. Institute of Electrical and Electronics Engineers Inc. 2018. p. 1-6 https://doi.org/10.1109/CISS.2018.8362267
Dunyak, Alex ; Zhu, Quanyan. / Understanding mean-field effects of large-population user data obfuscation in machine learning. 2018 52nd Annual Conference on Information Sciences and Systems, CISS 2018. Institute of Electrical and Electronics Engineers Inc., 2018. pp. 1-6
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