A Privacy Enhancing Approach for Identity Inference Protection in Location-Based Services

Recent advances in mobile handheld devices have facilitated the ubiquitous availability of location based services. Systems which provide location based services have always been vulnerable to numerous privacy threats. The more we aim at safe usage of location based services, the more we feel the necessity of a secure location privacy system. Most of the existing systems adopt the mechanism of satisfying k-anonymity which means that the exact user remains indistinguishable among k-1 other users. These systems usually propose the usage of a location anonymizer (LA) to achieve k-anonymity. In this paper we show that satisfying k-anonymity is not enough in preserving location privacy violation. Especially in an environment where a group of colluded service providers collaborate with each other, a user's privacy can be compromised. We present a detailed analysis of such attack on privacy and propose a novel and powerful privacy definition called s-proximity. In addition to building a formal definition for s-proximity, we show that it is practical and it can be incorporated efficiently into existing systems to make them secure.