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Protecting users' location information in location-based services, also termed location privacy, has recently garnered significant attention due to its importance in satisfying users' privacy concerns when using location-aware services. Several approaches proposed in the literature blur the user's location in a region by increasing its spatial extent or anonymizing the user among several other users. Such approaches in nature require users to communicate through a trusted anonymizer for all of their queries which can impose unrealistic overall communication/computation overhead between the server and the anonymizer for users with more stringent privacy requirements. We revisit the location privacy problem with the objective of providing significantly more stringent privacy guarantees and propose SPIRAL, a Scalable Private Information Retrieval Approach to Location privacy, which is to the best of our knowledge, the first approach to utilize practical Private Information Retrieval (PIR) as a more fundamental approach to enable blind evaluation of range queries. We perform several experiments on real-world data to evaluate the effectiveness and the feasibility of our approach.

Abstract. In many data-publishing applications, the data owner needs to protect sensitive information pertaining to individuals. Meanwhile, certain information is required to be published. The sensitive information could be considered as leaked, if an adversary can infer the real value of a sensitive entry with a high confidence. In this paper we study how to protect sensitive data when an adversary can do inference attacks using association rules derived from the data. We formulate the inference attack model, and develop complexity results on computing a safe partial table. We classify the general problem into subcases based on the requirements of publishing information, and propose the corresponding algorithms for finding a safe partial table to publish. We have conducted an empirical study to evaluate these algorithms on real data.

With the advent of reliable positioning technologies and prevalence of location-based services, it is now feasible to accurately study the propagation of items such as infectious viruses, sensitive information pieces, and malwares through a population of moving objects, e.g., individuals, mobile devices, and vehicles. In such application scenarios, an item passes between two objects when the objects are sufficiently close (i.e., when they are, so-called, in contact), and hence once an item is initiated, it can penetrate the object population through the evolving network of contacts among objects, termed contact network. In this paper, for the first time we define and study reachability queries in large (i.e., disk-resident) contact datasets which record the movement of a (potentially large) set of objects moving in a spatial environment over an extended time period. A reachability query verifies whether two objects are "reachable" through the evolving contact network represented by such contact datasets. We propose two contact-dataset indexes that enable efficient evaluation of such queries despite the potentially humongous size of the contact datasets. With the first index, termed ReachGrid, at the query time only a small necessary portion of the contact network which is required for reachability evaluation is constructed and traversed. With the second approach, termed ReachGraph, we precompute reachability at different scales and leverage these precalculations at the query time for efficient query processing. We optimize the placement of both indexes on disk to enable efficient index traversal during query processing. We study the pros and cons of our proposed approaches by performing extensive experiments with both real and synthetic data. Based on our experimental results, our proposed approaches outperform existing reachability query processing techniques in contact networks by 76% on average.