大数据下基于数据挖掘的移动社交网络隐私保护技术研究

Jiawen Du, Yong Pi
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引用次数: 4

摘要

随着大数据时代的到来,人们的生活发生了翻天覆地的变化,不仅摆脱了繁琐的传统数据收集,而且可以直接从人们在社交网络上的足迹中收集和整理信息。本文对当前社交网络中的隐私问题进行了探讨和分析,提出了基于数据挖掘算法的用户隐私数据保护策略,真正确保社交网络中的用户隐私在大数据时代不被非法侵犯。本文提出的数据挖掘算法可以保护用户的身份不被识别,保护用户的隐私信息不被泄露。在社交网络中使用差分隐私保护方法可以有效地保护用户在数据发布和数据挖掘中的隐私信息。因此,研究基于差分隐私保护的数据发布、数据挖掘方法及其在社交网络中的应用具有重要意义。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Research on Privacy Protection Technology of Mobile Social Network Based on Data Mining under Big Data
With the advent of the era of big data, people’s lives have undergone earth-shaking changes, not only getting rid of the cumbersome traditional data collection but also collecting and sorting information directly from people’s footprints on social networks. This paper explores and analyzes the privacy issues in current social networks and puts forward the protection strategies of users’ privacy data based on data mining algorithms so as to truly ensure that users’ privacy in social networks will not be illegally infringed in the era of big data. The data mining algorithm proposed in this paper can protect the user’s identity from being identified and the user’s private information from being leaked. Using differential privacy protection methods in social networks can effectively protect users’ privacy information in data publishing and data mining. Therefore, it is of great significance to study data publishing, data mining methods based on differential privacy protection, and their application in social networks.
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