首页|Enhanced Privacy Preserving for Social Networks Relational Data Based on Personalized Differential Privacy
Enhanced Privacy Preserving for Social Networks Relational Data Based on Personalized Differential Privacy
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
万方数据
With the popularization and develop-ment of social software,more and more people join the social network,which produces a lot of valuable informa-tion,but also contains plenty of sensitive privacy informa-tion.To achieve the personalized privacy protection of masssive social network relational data,a privacy enhance-ment method for social networks relational data based on personalized differential privacy is proposed.And a di-mensionality reduction segmentation sampling(DRS-S)algorithm is proposed to implement this method.First,in order to solve the problem of inefficiency caused by the excessive amount of data in social networks,dimension re-duction and segmentation are carried out to divide the data into groups.According to the privacy protection re-quirements of different users,we adopt sampling method to protect users with different privacy requirements at different levels,so as to realize personalized different pri-vacy.After that,the noise is added to the protected data to satisfy the privacy budget.Then publish the social net-work data.Finally,the proposed algorithm is compared with the traditional personalized differential privacy(PDP)algorithm and privacy preserving approach based on clustering and noise(PBCN)in real data set,the ex-perimental results demonstrate that the quality of pri-vacy protection and data availability of DRS-S are better than that of PDP algorithm and PBCN algorithm.
Social networkPrivacy preservingDimensionality reduction segmentationPersonalized dif-ferential privacyDRS-S algorithm
KANG Haiyan、JI Yuanrui、ZHANG Shuxuan
展开 >
School of Information Management,Beijing Information Science and Technology University,Beijing 100192,China
Computer School,Beijing Information Science and Technology University,Beijing 100192,China
国家社会科学基金Ministry of Education of Humanities and Social Science Project国家自然科学基金
NETL
NSTL
万方数据
