We address issues related to privacy protection in location-based services (LBSs). Most existing privacy-preserving LBS techniques either require a trusted third-party (anonymizer) or use cryptographic protocols that are computationally and communicationally expensive. Our design of privacy-preserving techniques is principled on not requiring a trusted third-party while being highly efficient in terms of time and space complexities. The problem has two interesting and challenging characteristics: First, the degree of privacy protection and LBS accuracy depends on the context, such as population and road density, around a user's location. Second, an adversary may violate a user's location privacy in two ways: (i) based on the user's location information contained in the LBS query payload and (ii) by inferring a user's geographical location based on the device's IP address. To address these challenges, we introduce CAP, a context-aware privacy-preserving LBS system with integrated protection for both data privacy and communication anonymity. We have implemented CAP and integrated it with Google Maps, a popular LBS system. Theoretical analysis and experimental results validate CAP's effectiveness on privacy protection, LBS accuracy, and communication QoS (Quality-of-Service).
All Science Journal Classification (ASJC) codes
- Computer Networks and Communications