RobLoP: Towards Robust Privacy Preserving Against Location Dependent Attacks in Continuous LBS Queries - 2018

PROJECT TITLE :

RobLoP: Towards Robust Privacy Preserving Against Location Dependent Attacks in Continuous LBS Queries - 2018

ABSTRACT:

With the increasing popularity of location-based services (LBS), the way to preserve one's location privacy has become a key issue to be involved. The commonly used approach k-anonymity, originally designed for shielding a user's snapshot location privacy, inherently fails to preserve the user from location-dependent attacks (LDA) that include the utmost movement boundary (MMB) attacks and most arrival boundary (MAB) attacks, when the user continuously requests LBS. This Project presents RobLoP, a sturdy location privacy preserving algorithm against LDA in continuous LBS queries. The key insight of RobLoP is to theoretically derive the constraints of each MMB and MAB during a uniform means. It provides a necessary condition of the pairwise user to be safely cloaked against LDA. On top of that, RobLoP first identifies those candidate users who will be cloaked with the requesting user. RobLoP then searches for a thus-referred to as strict purpose set together with the candidate set and alternative auxiliary points, as a sufficient condition below that RobLoP can finally generate the cloaked region successfully. To the simplest of our information, RobLoP is the first work that can preserve location privacy against LDA totally and closely with a theoretical guarantee. The effectiveness and superiority of RobLoP to state-of-the-art studies are validated via extensive simulations on the important trucks data, the artificial knowledge, further because the measured data collected by ourselves.

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