On Designing Satisfaction-Ratio-Aware Truthful Incentive Mechanisms for $k$ -Anonymity Location Privacy
Yuan Zhang, Wei Tong, Sheng Zhong · IEEE Transactions on Information Forensics and Security · 2016
To protect individuals' location privacy, an important privacy protection technique that can be used is k -anonymity, which requires at least k users to participate in an anonymity set, so that any user in the set cannot be distinguished from the other k-1 users. However, a significant part of users may not be concerned about their location privacy and therefore may not be interested in participating in the anonymity set. Hence, a prerequisite for achieving k-anonymity location privacy is to stimulate users to participate. In this paper, we revisit the problem of stimulating users that are privacy-indifferent to participate in the anonymity set and providing k-anonymity location privacy for privacy-sensitive users. We first study the case where all privacy-sensitive users have the same requirement of privacy. Then, we extend our study to a more general setting, where privacy-sensitive users have different requirements. For both cases, we design auction-based mechanisms and rigorously prove that the mechanisms are truthful. More importantly, our mechanisms can achieve higher satisfaction ratio than the existing work, i.e., our mechanisms greatly increase the number of privacy-sensitive users successfully winning the auction and receiving privacy protection. We evaluate our mechanisms by using extensive numerical experiments and simulations on a real-world data set. Evaluation results show that our mechanisms achieve much better performance regarding the satisfaction ratio compared with the state-of-the-art mechanisms, and that the computational efficiency is good.