LP-BT: A location privacy protection algorithm based on ball trees

With the maturity of global positioning technology and the massive popularity of mobile terminals, location-based services can provide people with convenient and efficient assistance. To use such services, mobile users need to provide location information and request query content. However, this process inevitably leads to the leakage of users’ privacy information, which poses a great threat to their property and personal safety. To address the privacy leakage in location services, this paper proposes a location privacy protection method based on ball tree (LP-BT). We first use the ball tree as a spatial index structure, and then do fuzzification on the location information of end users to obtain the maximum primary anonymous entropy, and combine the neural network learning algorithm to predict the corresponding entropy value. Finally, the final entropy is obtained based on the average entropy of the two stages. Experimental results on public dataset manifest that our model is superior to other models such as random selection model and path-based fake location generation model in terms of privacy protection level, user density and anonymization time overhead.