基于最小化信息损失的用户隐私保护方法

在新兴电子商务发展过程中,对用户数据的收集、使用、开放与共享达到了前所未有的程度,给个人隐私安全带来了极大挑战.为了解决数据效用与个人隐私之间的矛盾,本文提出基于k-匿名原则的最小化信息损失隐私保护方法.首先结合属性阈值特征提出一种新的记录排序算法;接着将隐私保护过程转化为对各条记录与各个候选匿名函数之间的最优分配问题,构建最小化信息损失的优化模型,并设计启发式方法快速求解最优匿名函数的选择与分配方案,实现对数据的匿名处理.在三个不同规模的真实数据集上,通过与目前最有效的多个隐私保护方法进行数值实验比较.结果表明本文方法在满足相同隐私保护水平下可产生最佳的数据效用且具有较快的计算效率.本文方法为新兴电子商务中用户数据隐私保护研究提供了理论和技术上的创新,为大规模数据的隐私保护应用提供了有效的解决方案.

User privacy preservation approach based on minimum information loss

In the development of emerging e-commerce,the collection,utilization,openness and sharing of user data has reached an unprecedented level,which also brings great challenges to personal privacy security.In order to resolve the contradiction between user data usage and personal privacy protection,this study proposes a data privacy preservation approach that aims at minimizing the information loss,based on kanonymity principle.Firstly,a new record sorting algorithm is proposed based on the features of attribute domains.Secondly,the privacy protection process is transformed into an optimal allocation problem between the entire records and the available anonymization function candidates,thus an optimization model is constructed to minimize the total information loss generated by assigning different functions to anonymize every record.To reduce the computation time,a heuristic method is developed to solve the optimal allocation model and implement anonymization for each record.A numerical study is conducted on three real-world datasets of different scales,by comparing with the most advanced privacy protection methods in existing research to demonstrate the effectiveness of the proposed approach.The results show that this approach can produce maximum data utility and has superior computational efficiency over the benchmarks,under the same level of privacy protection.This research provides theoretical and technical innovations for privacy preserving user data in emerging e-commerce,and offers an effective solution for privacy protection in large data applications.