Another dummy generation technique in location-based services

With the proliferation of mobile devices, many users now take advantage of location-based services that use their current position. However, careful consideration should be made when sending one's location to another as the location often includes personal attributes such as home address and reveals private information such as health or religion. To resolve this issue, a dummy generation technique is widely used. This technique protects the location privacy of a user by generating false position data (dummy) along with the true position data to obfuscate an adversary. However, the current dummy generation technique rarely assumes any prior knowledge held by the attacker that may allow them to reduce the level of uncertainty about the true location. In this paper, we propose a dummy generation method that is resistant to adversaries who have information about the user as well as external spatiotemporal knowledge. Our method uses conditional probabilities to generate realistic false locations at which the user is highly likely to be located at the given time and add more weight to the vulnerable location and time pairs. We first describe the strategy for the adversary and present our dummy generation method which is simple and effective for preventing the described attack. Experimental results show that our method obfuscates the true location more successfully compared to other approaches.