Currently, many location-aware applications are available for mobile users of location-based services. Applications such as Google Now, Trace4You or FourSquare are being widely used in various environments where privacy is a critical issue for users. A general solution for preserving location privacy for a user is to degrade the quality of his or her position information. In this work, we propose an approach that uses spatial obfuscation to secure the users' position information. By revealing the user's position with a certain degree of obfuscation, the first crucial issue is the tradeoff between privacy and precision. This tradeoff problem is caused by limited trust in the location service providers: higher obfuscation increases privacy but leads to lower quality of service. We overcome this problem by introducing the position sharing approach. Our main idea is that position information is distributed amongst multiple providers in the form of separate data pieces called position shares. Our approach allows for the usage of non-trusted providers and flexibly manages the user's location privacy level based on probabilistic privacy metrics. In this work, we present the multi-provider based position sharing approach, which includes algorithms for the generation of position shares and share fusion algorithms.

The second challenge that must be addressed is that the user's environmental context can significantly decrease the level of obfuscation. For example, a plane, a boat and a car create different requirements for the obfuscated region. Therefore, it is very important to consider map-awareness in selecting the obfuscated areas. We assume that a static map is known to an adversary, which may help in deriving the user's true position. We analyze both how map-awareness affects the generation and fusion of position shares and the difference between the map-aware position sharing approach and its open space based version. Our security analysis shows that the proposed position sharing approach provides good security guarantees for both open space and constrained space based models.

The third challenge is that multiple location servers and/or their providers may have different trustworthiness from the user's point of view. In this case, the user would prefer not to reveal an equal level (precision) of position information to every server. We propose a placement optimization approach that ensures that risk is balanced among the location servers according to their individual trust levels. Our evaluation shows significant improvement of privacy guarantees after applying the optimized share distribution, in comparison with the equal share distribution.

The fourth related problem is the location update algorithm. A high number of different location servers n (corresponding to n privacy levels) may lead to significant communication overhead. Each update would require n messages from the mobile user to the location servers, especially in cases of high update rate. Therefore, we propose an optimized location update algorithm to decrease the number of messages sent without reducing the number of privacy levels and the user's privacy.