Abstract

Social network platform is the one where a huge number of information from social networks are published and accessed by third parties or with advertising partners for better social targeting. Hence published information must be deleted before the information is collected by recognizing people (anonymized). Thus the anonymization of data is more difficult and challenging and is a popular model to preserve privacy. This paper summarizes the anonymization techniques such as Graph modification techniques and Differential privacy that modifies the original graph structure to form the anonymous graph for privacy preservation in social networks.

中文翻译：

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图的社交网络隐私保护研究述评

摘要

社交网络平台是第三方或广告合作伙伴发布和访问大量来自社交网络的信息以更好地进行社交定位的平台。因此，在通过识别人员（匿名）收集信息之前，必须删除已发布的信息。因此，数据的匿名化更加困难和具有挑战性，并且是保护隐私的流行模型。本文总结了匿名化技术，例如图修改技术和差分隐私，它们修改了原始图结构以形成匿名图，用于在社交网络中保护隐私。