文档介绍：基于Skyline计算的社交网络关系数据隐私保护
摘要：随着社交软件的流行，越来越多的人加入社交网 络产生了大量有价值的信息，其中也包含了许多敏感隐私信息。 不同的用户有不同的隐私需求，因此需要不同级别的隐私保护。 社交网络中用户隐私泄露等级受社交网络图结构和用户自身威 胁等级等诸多因素的影响。针对社交网络数据的个性化隐私保护 问题及用户隐私泄露等级评价问题，提出基于Skyline计算的个 性化差分隐私保护策略(PDPS)用以发布社交网络关系数据。 首先构建用户的属性向量；接着采用基于Skyline计算的方法评 定用户的隐私泄露等级，并根据该等级对用户数据集进行分割； 然后应用采样机制来实现个性化差分隐私，并对整合后的数据添 加噪声;最后对处理后数据进行安全性和实用性的分析并发布数 据。在真实数据集上与传统的个性化差分隐私方法(PDP)对比, 验证了 PDPS算法的隐私保护质量和数据的可用性都优于PDP 算法。
关键词：社交网络;隐私保护；Skyline计算;个性化差分隐私； 基于Skyline计算的个性化差分隐私保护算法
中图分类号：TP309
文献标志码：A
Abstract： With the popularity and development of social software, more and more people join the social network, which produces a lot of valuable information, including sensitive private information. Different users have different private requirements and therefore require different levels of privacy protection. The level of user privacy leak in social network is affected by many factors, such as the structure of social network graph and the threat level of the user himself. Aiming at the personalized differential privacy preserving problem and user privacy leak level problem, a Personalized Differential Privacy based on Skyline (PDPS ) algorithm was proposed to publish social network relational data. Firstly, users attribute vector was built. Secondly, the user privacy leak level was calculated by Skyline computation method and the user dataset was segmented according to this level. Thirdly, w