Increasing the Anonymity of a Social Network Based on Splitting Users into Constant Usefulness and Zero Information Loss

Seyed Javad Vaez Jalali, A. Falahati
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Abstract

Social networks are a part of today's society. In such networks, privacy-preserving is considered as an important field since the user's identity is usually revealed by inference attacks. Within this context, security system designers use an isomorphic graph to stop attackers by editing nodes and links, so, the attackers cannot access to users' identities when the vertexes and links of the graph are converted into isomorphic parameters. The security system designers employ random links, clustering of the nodes, weight balancing, nodes addition or de-anonymization techniques (nodes labeling) to confuse malicious attackers. But, these techniques have many defects, such as the loss of information and the reduction of usefulness parameters that evaluate the final social network graph. This paper proposes a new method named as a splitting method where three techniques are proposed to improve mentioned parameters and in general, to improve further the network management.
基于用户持续有用和零信息丢失的社交网络匿名性提升研究
社交网络是当今社会的一部分。在这种网络中,隐私保护被认为是一个重要的领域,因为用户的身份通常会被推理攻击泄露。在这种情况下,安全系统设计者使用同构图通过编辑节点和链接来阻止攻击者,这样当图的顶点和链接转换为同构参数时,攻击者就无法访问用户的身份。安全系统设计者采用随机链接、节点聚类、权重平衡、节点添加或去匿名化技术(节点标记)来迷惑恶意攻击者。但是,这些技术存在信息丢失、评价最终社会网络图的有用性参数减少等缺陷。本文提出了一种新的方法,称为分割法,其中提出了三种技术来改善上述参数,总体上进一步改善网络管理。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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