Computer Science scholars develop enhanced privacy protection for use of location-based services provided by mobile social networks
14 Jan 2013
Dr. Xu Jianliang, Associate Professor; Dr. Hu Haibo, 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, using a grid-and-hash paradigm that significantly increases 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 those who use these mobile geo-social networks could easily be disclosed to or collected by service providers, leading to concerns about privacy protection.
The project by Dr. Xu and his team aims at proposing a new quantitative 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 ensure the security of their data collected by servers. There are also possible risks of disclosing unnecessary personal information to service providers. 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, 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 enabling 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.