摘要:事务数据常见于各种应用场景中，如购物记录、页面浏览历史等.为了提供更好的服务，服务提供商收集用户数据并进行分析，但收集事务数据会泄露用户的隐私信息.为了解决上述问题，基于压缩的本地差分隐私模型，提出一种事务数据收集方法.首先，定义了一种新的候选项集分值函数；其次，基于该函数，将候选项集的样本空间划分为多个子空间；然后，随机选择其中一个子空间，基于该子空间随机生成事务数据并发送给不可信的数据收集者；最后，考虑到隐私参数的设置问题，基于最大后验置信度攻击模型设计启发式隐私参数设置策略.理论分析表明，该方法能够同时保护事务数据的长度与内容，满足压缩的本地差分隐私要求.实验结果表明，与目前最优的工作相比，所收集的数据具有更高的效用性，隐私参数设置更具有语义性.



Abstract:Transaction data is commonly in various application scenarios, such as shopping records, page browsing history, etc., service providers collect and analyze transaction data for providing better services. However, collecting transaction data will disclose privacy information. To solve the problem, this study proposes a transaction data collection mechanism based on condensed local differential privacy (CLDP). Firstly, a new score function of the candidate set is defined. Secondly, the output domain of the candidate set is separated into several subspaces according to the function. Thirdly, the client selects one subspace randomly, and generates transaction data randomly based on the subspace, then, sends it to the untrusted data collector. Finally, considering the difficulty for setting the privacy parameter, the heuristic privacy parameter setting strategy is designed based on the maximum posterior confidence threat model (MPC). The theoretical analysis shows that this method can protect the length and content of transaction data at the same time and satisfies a-CLDP. The experiments demonstrate that the transaction data collected in this study has higher utility than the state-of-the-art approaches, and the privacy parameter setting is semantic.



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