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A location histogram is comprised of the number of times a user has visitedlocations as they move in an area of interest, and it is often obtained fromthe user in applications such as recommendation and advertising. However, alocation histogram that leaves a user's computer or device may threaten privacywhen it contains visits to locations that the user does not want to disclose(sensitive locations), or when it can be used to profile the user in a way thatleads to price discrimination and unsolicited advertising. Our work introducestwo privacy notions to protect a location histogram from these threats:sensitive location hiding, which aims at concealing all visits to sensitivelocations, and target avoidance/resemblance, which aims at concealing thesimilarity/dissimilarity of the user's histogram to a target histogram thatcorresponds to an undesired/desired profile. We formulate an optimizationproblem around each notion: Sensitive Location Hiding (SLH), which seeks toconstruct a histogram that is as similar as possible to the user's histogrambut associates all visits with nonsensitive locations, and TargetAvoidance/Resemblance (TA/TR), which seeks to construct a histogram that is asdissimilar/similar as possible to a given target histogram but remains usefulfor getting a good response from the application that analyzes the histogram.We develop an optimal algorithm for each notion and also develop a greedyheuristic for the TA/TR problem. Our experiments demonstrate that allalgorithms are effective at preserving the distribution of locations in ahistogram and the quality of location recommendation. They also demonstratethat the heuristic produces near-optimal solutions while being orders ofmagnitude faster than the optimal algorithm for TA/TR.

Location histogram privacy by sensitive location hiding and target histogram avoidance/resemblance (extended version)