Bloom filter-based lightweight private matching scheme

Sheng WAN; Yuan-yuan HE; Feng-hua LI; Ben NIU; Hui LI; Xin-yu WANG

doi:10.11959/j.issn.1000-436x.2015324

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Bloom filter-based lightweight private matching scheme

privacy protection | 更新时间：2024-06-05

- Bloom filter-based lightweight private matching scheme
- Journal on Communications Vol. 36, Issue 12, Pages: 151-162(2015)
- 作者机构：
  
  1. 西安电子科技大学综合业务网理论及关键技术国家重点实验室，陕西西安 710071
  2. 中国科学院信息工程研究所信息安全国家重点实验室，北京 100195
  3. 北京电子科技学院信息安全系，北京 100070
- 作者简介：
- 基金信息：
  
  The National Natural Science Foundation of China –Guangdong Union Foundation(U1401251);The National High Technology Research and Development Program of China (863 Program)(2012AA013102);The Key Program of Scientific and Technology Research of Ministry of Education(209156)
- DOI：10.11959/j.issn.1000-436x.2015324
  CLC： TN929
- Online First：2015-12，
  
  Published：25 December 2015
- 稿件说明：
移动端阅览
Sheng WAN, Yuan-yuan HE, Feng-hua LI, et al. Bloom filter-based lightweight private matching scheme[J]. Journal on Communications, 2015, 36(12): 151-162.
DOI：

Sheng WAN, Yuan-yuan HE, Feng-hua LI, et al. Bloom filter-based lightweight private matching scheme[J]. Journal on Communications, 2015, 36(12): 151-162. DOI： 10.11959/j.issn.1000-436x.2015324.

摘要

针对智能终端用户私有数据匹配中的隐私保护问题，基于布隆过滤器和二元向量内积协议，提出一种新的综合考虑用户属性及其偏好的轻量级隐私信息匹配方案，包括建立基于 Dice 相似性系数的二维向量相似度函数、设置参数、生成布隆过滤器、计算二元向量内积、计算相似度和确定匹配对象6个部分。该方案采用基于布隆过滤器的相似度估计和基于混淆方法的二元向量内积协议，在不依赖于可信第三方的前提下，大幅度降低计算开销，且能够有效抵御蛮力攻击和无限制输入攻击。实验结果表明，该方案与典型代表方案相比，计算效率得到明显提升。

Abstract

With rapid developments of mobile devices and online social networks

users of proximity-based mobile social networks (PMSN) could easily discover and make new social interactions with others

but they enjoyed this kind of conveniences at the cost of user privacy and system overhead

etc.To address this problem

a third party free and lightweight scheme to privately match the similarity with potential friends in vicinity was proposed.Unlike most existing work

proposed scheme considered both the number of common attributes and the corresponding priorities on each of them individually.The Bloom filter-based common-attributes estimation and the lightweight confusion binary vector scalar product protocol reduce the system overhead significantly

and can resist against brute force attack and unlimited input attack.The correctness

security and performance of overhead of proposed scheme are then thoroughly analyzed and evaluated via detailed simulations.

关键词

Keywords

references

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