PLP: Protecting Location Privacy Against Correlation-Analysis Attack in Crowdsensing

Abstract

Crowdsensing applications require individuals toshare local and personal sensing data with others to produce valuableknowledge and services. Meanwhile, it has raised concernsespecially for location privacy. Users may wish to prevent privacyleak and publish as many non-sensitive contexts as possible.Simply suppressing sensitive contexts is vulnerable to the adversariesexploiting spatio-temporal correlations in users' behavior.In this work, we present PLP, a crowdsensing scheme whichpreserves privacy while maximizes the amount of data collectionby filtering a user's context stream. PLP leverages a conditionalrandom field to model the spatio-temporal correlations amongthe contexts, and proposes a speed-up algorithm to learn theweaknesses in the correlations. Even if the adversaries are strongenough to know the filtering system and the weaknesses, PLPcan still provably preserves privacy, with little computationalcost for online operations. PLP is evaluated and validated overtwo real-world smartphone context traces of 34 users. Theexperimental results show that PLP efficiently protects privacywithout sacrificing much utility.