计算机工程 ›› 2019, Vol. 45 ›› Issue (4): 114-118.doi: 10.19678/j.issn.1000-3428.0049695

• 安全技术 • 上一篇    下一篇

权重社交网络隐私保护中的差分隐私算法

王丹a,b,龙士工a,b   

  1. 贵州大学 a.贵州省公共大数据重点实验室; b.计算机科学与技术学院,贵阳 550025
  • 收稿日期:2017-12-13 出版日期:2019-04-15 发布日期:2019-04-15
  • 作者简介:王丹(1990—),女,硕士研究生,主研方向为差分隐私保护;龙士工,教授。
  • 基金项目:

    贵州省公共大数据重点实验室开放项目(2017001)。

Differential Privacy Algorithm for Privacy Protection in Weighted Social Network

WANG Dana,b,LONG Shigonga,b   

  1. a.Guizhou Provincial Key Laboratory of Public Big Data; b.College of Computer Science and Technology,Guizhou University,Guiyang 550025,China
  • Received:2017-12-13 Online:2019-04-15 Published:2019-04-15

摘要:

针对社交网络的边权重隐私泄露问题,提出一种权重社交网络隐私保护算法。利用无向有权图表示社交网络,把边权重序列作为一个无归属直方图处理,将包含敏感信息的权重加入拉普拉斯噪声以满足差分隐私保护要求。为减少噪音量,对直方图中具有相同计数的桶合并成组,根据组间k-不可区分性来保证差分隐私保护要求,通过对原始的权重序列进行一致性推理保持网络最短路径不变。理论分析和实验结果表明,该算法能够满足差分隐私保护要求,且提高了信息发布的准确性和实用性。

关键词: 社交网络, 差分隐私, 隐私保护, 最短路径, 边权重

Abstract:

In order to solve the edge weight privacy leakage problem of social network,a privacy protection algorithm for weighted social network is proposed.The undirected weighted graph is used to represent the social network,the edge weight sequence is treated as an unassigned histogram,and the weight containing sensitive information is added to the Laplace noise to satisfy the differential privacy protection requirement.In order to reduce noise,the buckets with the same count in histogram are merged into groups,and the differential privacy protection requirements are guaranteed according to the k-indiscernibility between groups,and the shortest path of the network is kept unchanged by consistent reasoning on the original weight sequence.Theoretical analysis and experimental results show that the proposed algorithm can meet the requirements of differential privacy protection and improve the accuracy and practicability of information release.

Key words: social network, differential privacy, privacy protection, shortest path, edge weight

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