TIIS (한국인터넷정보학회)
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| 한글제목(Korean Title) |
Privacy Protection Method for Sensitive Weighted Edges in Social Networks |
| 영문제목(English Title) |
Privacy Protection Method for Sensitive Weighted Edges in Social Networks |
| 저자(Author) |
Weihua Gong
Rong Jin
Yanjun Li
Lianghuai Yang
Jianping Mei
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| 원문수록처(Citation) |
VOL 15 NO. 02 PP. 0540 ~ 0557 (2021. 02) |
한글내용
(Korean Abstract) |
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영문내용
(English Abstract) |
Privacy vulnerability of social networks is one of the major concerns for social science research and business analysis. Most existing studies which mainly focus on un-weighted network graph, have designed various privacy models similar to k-anonymity to prevent data disclosure of vertex attributes or relationships, but they may be suffered from serious problems of huge information loss and significant modification of key properties of the network structure. Furthermore, there still lacks further considerations of privacy protection for important sensitive edges in weighted social networks. To address this problem, this paper proposes a privacy preserving method to protect sensitive weighted edges. Firstly, the sensitive edges are differentiated from weighted edges according to the edge betweenness centrality, which evaluates the importance of entities in social network. Then, the perturbation operations are used to preserve the privacy of weighted social network by adding some pseudo-edges or modifying specific edge weights, so that the bottleneck problem of information flow can be well resolved in key area of the social network. Experimental results show that the proposed method can not only effectively preserve the sensitive edges with lower computation cost, but also maintain the stability of the network structures. Further, the capability of defending against malicious attacks to important sensitive edges has been greatly improved.
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| 키워드(Keyword) |
Social Network
Privacy Protection
Sensitive Weighted Edges
Edge Betweenness
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| 파일첨부 |
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