Privacy-preserving crowdsensing: Privacy valuation, network effect, and profit maximization

Mengyuan Zhang, Lei Yang, Xiaowen Gong, Junshan Zhang

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

13 Scopus citations

Abstract

In spite of the pronounced benefit brought by crowdsensing, a user would not participate in sensing without adequate incentive, indicating that effective incentive design plays a critical role in making crowdsensing a reality. In this work, we examine the impact of two conflicting factors on incentives for users' participation: 1) the concern about privacy leakage and 2) the (positive) network effect from many sensing participants. The former factor hinders privacy- aware users from participating, whereas the latter encourages users' participation. Taking into consideration both factors, we devise a privacy-preserving crowdsensing scheme, in which a reverse 'privacy' auction is first run by the crowdsensing platform to select users based on their privacy valuations and the network effect. Then the trusted platform carries out differentially private data aggregation over the collected data such that the released sensing result remains useful for the task agent, while all participants' data privacy is guaranteed. A natural objective here is then to maximize the profit of the task agent, i.e., the difference between its utility and the total reward to the participants. To this end, the platform utilizes a random-sampling based mechanism for the 'privacy' auction, followed by a Laplace mechanism for data aggregation. We show that this auction mechanism design is 4-competitive, and further it exhibits desirable properties, including individual rationality, truthfulness, computational efficiency. Simulation results corroborate the theoretical properties of the proposed privacy-preserving crowdsensing scheme.

Original languageEnglish (US)
Title of host publication2016 IEEE Global Communications Conference, GLOBECOM 2016 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781509013289
DOIs
StatePublished - Feb 2 2017
Event59th IEEE Global Communications Conference, GLOBECOM 2016 - Washington, United States
Duration: Dec 4 2016Dec 8 2016

Other

Other59th IEEE Global Communications Conference, GLOBECOM 2016
CountryUnited States
CityWashington
Period12/4/1612/8/16

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Computer Networks and Communications
  • Hardware and Architecture
  • Safety, Risk, Reliability and Quality

Fingerprint Dive into the research topics of 'Privacy-preserving crowdsensing: Privacy valuation, network effect, and profit maximization'. Together they form a unique fingerprint.

  • Cite this

    Zhang, M., Yang, L., Gong, X., & Zhang, J. (2017). Privacy-preserving crowdsensing: Privacy valuation, network effect, and profit maximization. In 2016 IEEE Global Communications Conference, GLOBECOM 2016 - Proceedings [7842170] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/GLOCOM.2016.7842170