基于部分重编码的流数据发布隐私保护算法

Privacy Preserving Algorithm Based on Partial Re-encode of Streaming Data

针对流数据具有变化无常、流动极快、潜在无限等特征,相比静态数据隐私保护难度更大的问题,在流数据的基础上提出一种新的数据信息匿名算法,解决了敏感值及其敏感等级随数据转变而转变的难题,能有效地避免匿名流数据遭受链接攻击、相似性攻击以及基于敏感分级的链接攻击威胁.仿真实验结果表明,该流数据匿名模型可有效地保护数据的匿名信息.

Aiming at the problem that the streaming data were constantly changing, fast and potentially unlimited features, and it was more difficult to protect than static data privacy. Based on streaming data, we proposed a new data information anonymous algorithm to solve the problem of sensitive value and its sensitivity level changing with data transformation. It could effectively prevent anonymous streaming data from being linked attacks, similarity attacks and threat attack based on sensitive classification. The results of simulation experiment show that the new data anonymous model can effectively protect the anonymous information of the data.