HKBU CS Scholars Develop Enhanced Privacy Protection for Use of Location-based Services Provided by Mobile Social Networks

Dr. Xu Jianliang (徐建良), Associate Professor; Dr. Hu Haibo (胡海波), Research Assistant Professor; and their team at the Department of Computer Science have developed an improved version of the existing location-based services called "Nearby Friend Alert Service". The improved version shows significant increase in detection accuracy while not disclosing users’ locations to mobile service providers. The research findings provide insights on possible ways to develop the technique into services for location-based social networks.

Smartphones and tablets enable users to use location-related applications for route searches, to find nearby restaurants, or to “check-in” on social networks. However, the personal data and location information of users can easily be disclosed to or collected by service providers, leading to concerns about privacy protection.

The project conducted by Dr. Xu and his team proposes a new solution to the proximity detection system. Dr. Xu said: “The existing location-based services require users to disclose location data to their mobile service providers. Users have no way to secure those data collected by servers, while unnecessary personal information may also be leaked. For example, a service provider could associate a user who goes to a specialty health clinic frequently with certain illnesses.”

The team used computation and cryptography to come up with their nearby friend alert service. Dynamic grid overlay methods were used to detect proximity by dynamically quantifying each user’s mobile location as a grid cell identified by a cryptographic hash function. When a user sends a request looking for friends nearby, the service provider detects proximity, and sends back results in a cryptographic form to the requesting user. In this way, the server carries out computation without obtaining the users’ location data. The dynamic grid overlay technique significantly increases detection accuracy while saving wireless bandwidth.

Dr. Xu believes that the findings provide mutual benefits to users and service providers by preventing users from disclosing unnecessary personal information to service providers, who can in turn apply the techniques to new mobile applications emphasising user safety. In the future, the team hopes to further enhance proximity detection techniques which enable mobile users to receive information in a quicker, safer and better protected manner.

The findings will be published in the renowned academic magazine IEEE Pervasive Computing.