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An increasing number of location-based service providers are taking the advantage of cloud computing by outsourcing their Point of Interest (POI) datasets and query services to third-party cloud service providers (CSPs), which answer various location-based queries from users on their behalf. A critical security challenge is to ensure the integrity and completeness of any query result returned by CSPs. As an important type of queries, a location-based skyline query (LBSQ) asks for the POIs not dominated by any other POI with respect to a given query position, i.e., no POI is both closer to the query position and more preferable with respect to a given numeric attribute. While there have been several recent attempts on authenticating outsourced LBSQ, none of them support the shortest path distance that is preferable to the Euclidian distance in metropolitan areas. In this paper, we tackle this open challenge by introducing AuthSkySP, a novel scheme for authenticating outsourced LBSQ under the shortest path distance, which allows the user to verify the integrity and completeness of any LBSQ result returned by an untrusted CSP. We confirm the effectiveness and efficiency of our proposed solution via detailed experimental studies using both real and synthetic datasets. 

Indoor navigation is necessary for users to explore large unfamiliar indoor environments such as airports, shopping malls, and hospital complex, which relies on the capability of continuously tracking a user's location. A typical indoor navigation system is built on top of a suitable Indoor Positioning System (IPS) and requires the user to periodically submit location queries to learn their whereabouts whereby to provide update-to-date navigation information. Received signal strength (RSS)-based IPSes are considered as one of the most classical IPSes, which locates a user by comparing the user's RSS measurement with the fingerprints collected at different locations in advance. Despite its significant advantages, existing RSS-IPSes suffer from two key challenges, the ambiguity of RSS fingerprints and device diversity, that may greatly reduce its positioning accuracy. In this paper, we introduce the design and evaluation of CITS, a novel RSS-based continuous indoor tracking system that can effectively cope with fingerprint ambiguity and device diversity via differential RSS fingerprint matching. Detailed experiment studies confirm the significant advantages of CITS over prior RSS-based solutions.