A privacy-enhancing scheme against contextual knowledge-based attacks in location-based services

1 Introduction and main contributions Location-based services are springing up around us,while leakages of users'privacy are inevitable during services.Even worse,adversaries may analyze intercepted service data,and extract more privacy like health and property.Therefore,privacy preservation is an indispensable guarantee on LBS security.Among the previous approaches to privacy preservation,k-anonymity-based ones have drawn much research attention[1-3].However,some privacy concern will be aroused if these schemes are adopted directly.For instance,Ut issues a query"Find the nearest hotel around me"in such an area as Fig.1(privacy profile k=4).DLS algorithm[2]constructs anonymity set A because these four cells have similar probabilities of being queried in the past.However,experienced adversaries can exclude some cells if they have learned rich contextual knowledge(side information)from historical data,such as features of each cell and LBS users.