PEAK: Privacy-Enhanced Incentive Mechanism for Distributed K-Anonymity in LBS
Man Zhang, Xinghua Li, Yinbin Miao, Bin Luo, Yanbing Ren, Siqi Ma · IEEE Transactions on Knowledge and Data Engineering · 2023
To motivate users' assistance for protecting others' location privacy by distributedK-anonymity in Location-Based Service (LBS), many incentive mechanisms have been proposed, where users obtain monetary compensation for their assistance. However, most existing distributedK-anonymity incentive mechanisms rely on trusted third parties and ignore users' malicious strategies, which destroys LBS's distributed structure as well as leads to users' privacy leakage and incentive ineffectiveness. To solve the above problems, we propose aPrivacy-Enhanced incentive mechAnism for distributedK-anonymity (PEAK). With determining the monetary transaction relationship and location transmission between users, PEAK enables the anonymous cloaking region construction without the trusted server. Meanwhile, PEAK devises role identification mechanism and accountability mechanism to restrain and punish malicious users, which protects users' location privacy and implements effective motivation on users' assistance. Theoretical analysis based on the game theory shows that PEAK constrains users' malicious strategies while satisfying individual rationality, computational efficiency, and satisfaction ratio. Extensive experiments based on the real-world dataset demonstrate that PEAK improves security and feasibility, especially reaching the success rate of anonymous cloaking region construction to more than 90$\%$and decreasing the malicious users' utilities significantly.