基于双向拍卖的k-匿名激励机制

Double-auction-based Incentive Mechanism for k-anonymity

在基于位置的服务(LBS)中,k-匿名是重要的位置隐私保护技术之一。k-匿名要求至少k名用户参与匿名集的构建,使得集合中任何用户都不能从其他k-1名用户中区分开来。然而,很多参与者希望得到回报或顾忌个人隐私泄漏,导致匿名集人数不足。为了提高用户参与匿名集构建的积极性,提出了一种基于双向拍卖的k-匿名激励机制(Double-Acution-based Incentive,DAI),以保证交易公平的同时最大化买卖双方的效用。首先,利用多阶段采样来筛选候选用户集;然后,根据预算平衡性选择获胜用户集和合理的报酬;最后,从个体理性、计算效率、预算平衡和真诚可信等方面,通过理论证明了机制的合理性。仿真结果表明,DAI能够抑制用户恶意竞价情况的发生,同时提高买方的满意度和效用。

k- anonymity has become one of the most important location privacy technologies in LBS (Location Based Service).At least k users should be required to build an anonymous set,in which any user cannot be distinguished from other k-1 users.However,many users are not interested in their location privacy,so they have little interest in participating in the construction of anonymous sets.In order to improve the enthusiasm of users to participate in building anonymous sets,this paper proposed a double-auction-based incentive(DAI) mechanism for k -anonymity,which maximizes both the utility of buyers and sellers while guaranteeing fair transaction.To this end,multi-stage sample is used to filter the candidate user sets,then a reasonable remuneration and the winning set of users are determined according to budget balance.Finally,the rationality of the mechanism is provided in consideration of individual rationality,computation efficiency,budget balance and truthfulness,and so on.Simulation results demonstrate that DAI can solve the problem of malicious competition in the existing methods,and improve satisfaction and utility of buyers effectively.