Abstract
The existing location privacy protection methods only provide the privacy protection parameter k for users to choose to achieve their personalized privacy protection requirements, but users can not control the quality of service. To this end, this thesis proposes a user personalized location k anonymity privacy protection scheme with controllable service quality. The quality of service is quantified according to service similarity, and both it and the degree of privacy protection are used as user-controllable parameters, which meets the user’s more personalized privacy protection needs. At the same time, the background knowledge of historical query probability is quantified. The greedy strategy combined with the position entropy measurement mechanism is used to generate a k-anonymity set that can resist the attacker’s background knowledge inference attack, and a perturbation location is selected to complete the anonymity. The performance comparison experiment results show that this method can obtain very good privacy protection performance and efficiency.
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Liu, T., Yan, G., Cai, G., Wang, Q., Yang, M. (2020). User Personalized Location k Anonymity Privacy Protection Scheme with Controllable Service Quality. In: Chen, X., Yan, H., Yan, Q., Zhang, X. (eds) Machine Learning for Cyber Security. ML4CS 2020. Lecture Notes in Computer Science(), vol 12486. Springer, Cham. https://doi.org/10.1007/978-3-030-62223-7_42
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