Haotian Chi

P-Match: Priority-Aware Friend Discovery for Proximity-Based Mobile Social Networks

With rapid developments of mobile devices and online social networks, users of Proximity-based Mobile Social Networks (PMSNs) can easily discover and make new social interactions with others at the cost of their growing privacy concerns. To address this problem, we propose a third party free scheme, P-match, to privately match the similarity with potential friends in vicinity. Unlike most existing work, P-match considers both the number of common interests and the corresponding priorities on each of them individually. The security and performance overhead of our scheme are then thoroughly analyzed and evaluated via detailed simulations.

FineRoute: Personalized and Time-Aware Route Recommendation Based on Check-Ins

The rapid expansion of urbanization and the fast pace of life result in abundant choices with little time for people to manage routes. A proper planning route enables us to enjoy life better with fewer time and energy costs. Therefore, route planning becomes too valuable to be ignored. At the same time, with the popularity of mobile devices, location sensing, and Web 2.0 technologies, location-based social networks (e.g., Facebook Palaces and Foursquare) have attracted millions of users to share their visited locations and other information, which generates large amounts of user check-in data. These data can be used to mine users’ preferences and time information for recommending routes. In this paper, we propose FineRoute, a personalized and time-sensitive route recommendation system. We take three factors users’ preferences, proper visiting time, and transition time into consideration for the route generation. First, we infer users’ preferences by constructing a three-dimensional tensor, with three dimensions representing users, locations, and time, respectively. Second, we obtain the proper visiting time for certain locations, as well as the transition time between two locations from the check-in dataset. Moreover, we adopt Kullback–Leibler divergence in order to measure the quality of a route in terms of the proper visiting month and the proper visiting hour. Finally, we propose a route generation algorithm by extending the classic longest path algorithm. We conduct experiments on a real-world check-in dataset and the results demonstrate the effectiveness of our scheme.

Privacy-preserving use of genomic data on mobile devices

The rapid development of genomic technologies, especially the DNA sequencing progress, greatly decreases the costs in genome sequencing, which leads to a high availability of genomic data in the near future (e.g., medical test and healthcare). Meanwhile, the increasing popularity of smartphones and mobile connectivity makes it an inevitable trend to use genomic data on mobile devices. However, since ones genomic information is the ultimate identifier of an individual and contains sensitive private data (e.g., disease predispositions, ancestry information, etc.), the use of genomic data may raise serious privacy concerns. Therefore, it is necessary to find ways of using genomic data on mobile devices without disclosure and abuse of the individuals privacy. In this paper, we provide a general framework for storing and processing genomic data privately, in which dynamic symmetric searchable encryption (DSSE) is employed for users to efficiently retrieve their needed genomic data from the Cloud Storage Provider (CSP). Then, users can implement the genetic disease susceptibility test with these genomic data on their mobile devices. Our proposed framework can achieve a higher privacy preserving level at a lower computation cost. Performance evaluation shows the effectiveness and practicality of the proposed scheme.