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Mobile Information Systems
Volume 2016, Article ID 7867206, 13 pages
http://dx.doi.org/10.1155/2016/7867206
Research Article

Extended Privacy in Crowdsourced Location-Based Services Using Mobile Cloud Computing

1Computer Science Department, Notre Dame University-Louaize, Zouk Mosbeh, P.O. Box 72, Lebanon
2UPMC, Sorbonne University, LIP6, Paris, France
3Faculty of Sciences, Lebanese University, LARIFA-EDST, Pierre Gemayel Campus, Fanar, Lebanon
4Telecom SudParis, Institut Telecom, CNRS SAMOVAR, UMR 5751, 9 rue Charles Fourier, 91011 Evry, France

Received 28 January 2016; Accepted 19 June 2016

Academic Editor: Michele Amoretti

Copyright © 2016 Jacques Bou Abdo et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Crowdsourcing mobile applications are of increasing importance due to their suitability in providing personalized and better matching replies. The competitive edge of crowdsourcing is twofold; the requestors can achieve better and/or cheaper responses while the crowd contributors can achieve extra money by utilizing their free time or resources. Crowdsourcing location-based services inherit the querying mechanism from their legacy predecessors and this is where the threat lies. In this paper, we are going to show that none of the advanced privacy notions found in the literature except for -anonymity is suitable for crowdsourced location-based services. In addition, we are going to prove mathematically, using an attack we developed, that -anonymity does not satisfy the privacy level needed by such services. To respond to this emerging threat, we will propose a new concept, totally different from existing resource consuming privacy notions, to handle user privacy using Mobile Cloud Computing.