高效可验证的隐私保护推荐系统

宋春芝; 董晓蕾; 曹珍富

doi:10.3969/j.issn.1000-5641.2018.02.005

高效可验证的隐私保护推荐系统

doi: 10.3969/j.issn.1000-5641.2018.02.005

华东师范大学计算机科学与软件工程学院, 上海 200062

基金项目:

国家自然科学基金 61602180

国家自然科学基金 61632012

国家自然科学基金 61672239

上海市自然科学基金 16ZR1409200

上海市高新技术领域项目 16511101400

详细信息

作者简介:
宋春芝, 女, 硕士研究生, 研究方向为密码学与网络安全.E-mail:734974276@qq.com

通讯作者:
董晓蕾, 女, 教授, 博士生导师, 研究方向为密码学与网络安全.E-mail:dongxiaolei@sei.ecnu.edu.cn

中图分类号: TP309.7
计量
- 文章访问数: 184
- HTML全文浏览量: 65
- PDF下载量: 402
- 被引次数: 0
出版历程
- 收稿日期: 2017-06-25
- 刊出日期: 2018-03-25

Efficient verifiable privacy-preserving recommendation system

School of Computer Science and Software Engineering, East China Normal University, Shanghai 200062, China

摘要

摘要: 针对个性化推荐服务系统存在的隐私泄露问题，提出了一个高效可验证的隐私保护推荐系统，能在保护用户数据隐私的前提下，实现用户对云端计算出的推荐模型的正确性验证；利用脊回归实现对用户数据的拟合；利用Yao的混淆电路技术实现推荐模型的计算以及对模型的正确性验证.用户端和云端使用一种新的数据聚合算法AGG（Aggregation）来替换大多数已有工作中使用的公钥同态加密算法，减少了用户端和云端的计算开销，使得系统效率更高.给出了方案的安全性分析以及效率分析.
- 个性化推荐系统 /
- 脊回归 /
- 隐私保护 /
- 混淆电路 /
- 可验证计算
Abstract: To address the problem of privacy disclosure in traditional personalized recommendation systems, this paper proposes an efficient verifiable privacy-preserving recommendation system, which can provide user the way to verify the correctness of the resulting model of cloud computing under the premise of protecting user's data privacy. This paper uses ridge regression to find the best-fit linear curve of user's input data, and implements Yao's garbled circuit to realize the computation and the correctness verification of the recommendation model. The user and the cloud use a newly-devised privacy preserving data aggregation method named AGG (Aggregation) to replace public key homomorphic encryption used in most existing work, which can reduce the computational overhead of the user and the cloud, thus making the system more efficient. The security analysis and the efficiency analysis of the scheme are given at the end of the article.
- personalized recommendation system /
- ridge regression /
- privacy preservation /
- garbled circuits /
- verifiable computation

HTML全文

图 1 系统网络架构

Fig. 1 Network architecture of the system

下载: 全尺寸图片幻灯片

图 2 混淆电路

Fig. 2 Example of a garbled gate

下载: 全尺寸图片幻灯片

图 3 系统详述

Fig. 3 High-level description of the system

下载: 全尺寸图片幻灯片

参考文献(18)

[1]	ZHAO Z D, SHANG M S. User-based collaborative-filtering recommendation algorithms on hadoop[C]//Knowledge Discovery and Data Mining, WKDD'10, 3rd International Conference on. IEEE, 2010: 478-481.
[2]	邓爱林, 朱扬勇, 施伯乐.基于项目评分预测的协同过滤推荐算法[J].软件学报, 2003, 14(9):1621-1628. http://kns.cnki.net/KCMS/detail/detail.aspx?filename=rjxb200309017&dbname=CJFD&dbcode=CJFQ
[3]	SARWAR B, KARYPIS G, KONSTAN J, et al. Item-based collaborative filtering recommendation algorithms[C]//Proceedings of the 10th International Conference on World Wide Web. ACM, 2001: 285-295.
[4]	PAVLOV D, PENNOCK D M. A maximum entropy approach to collaborative filtering in dynamic, sparse, high-dimensional domains[C]//NIPS. 2002, 2: 1441-1448.
[5]	MARLIN B M. Modeling user rating profiles for collaborative filtering[C]//Advances in Neural Information Processing Systems. 2004: 627-634.
[6]	NIKOLAENKO V, IOANNIDIS S, WEINSBERG U, et al. Privacy-preserving matrix factorization[C]//Proceedings of the 2013 ACM SIGSAC Conference on Computer & Communications Security. ACM, 2013: 801-812.
[7]	SHYONG K, FRANKOWSKI D, RIEDL J. Do you trust your recommendations? An exploration of security and privacy issues in recommender systems[M]//Emerging Trends in Information and Communication Security. Berlin: Springer, 2006: 14-29.
[8]	AÏMEUR E, BRASSARD G, FERNANDEZ J M, et al. Alambic:A privacy-preserving recommender system for electronic commerce[J]. International Journal of Information Security, 2008, 7(5):307-334. doi: 10.1007/s10207-007-0049-3
[9]	MCSHERRY F, MIRONOV I. Differentially private recommender systems: Building privacy into the net[C]//Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 2009: 627-636.
[10]	MOBASHER B, BURKE R, BHAUMIK R, et al. Toward trustworthy recommender systems:An analysis of attack models and algorithm robustness[J]. ACM Transactions on Internet Technology (TOIT), 2007, 7(4):23-38. doi: 10.1145/1278366
[11]	POLAT H, DU W. Privacy-preserving collaborative filtering using randomized perturbation techniques[C]//IEEE Internatioal Conference on Data Mining. IEEE, 2005: 625-628.
[12]	GENTRY C. Fully homomorphic encryption using ideal lattices[C]//Proceedings of the 41st Annual ACM Symposium on Theory of Computing. 2009: 169-178.
[13]	YAO A C C. Protocols for secure computations[C]//Foundations of Computer Science, 1982, SFCS'08, 23rd Annual Symposium on. IEEE, 1982: 160-164.
[14]	YAO A C C. How to generate and exchange secrets[C]//Foundations of Computer Science, 1986, 27th Annual Symposium on. IEEE, 1986: 162-167.
[15]	GENNARO R, GENTRY C, PARNO B. Non-interactive verifiable computing: Outsourcing computation to untrusted workers[C]//Annual Cryptology Conference. Berlin: Springer, 2010: 465-482.
[16]	ZHOU J, CAO Z F, DONG X L, et al. Security and privacy for cloud-based IoT:Challenges[J]. IEEE Communications Magazine, 2017, 55(1):26-33. doi: 10.1109/MCOM.2017.1600363CM
[17]	NIKOLAENKO V, WEINSBERG U, IOANNIDIS S, et al. Privacy-preserving ridge regression on hundreds of millions of records[C]//Security and Privacy (SP), 2013 IEEE Symposium on. IEEE, 2013: 334-348.
[18]	PAILLIER P. Public-key cryptosystems based on composite degree residuosity classes[C]//International Conference on the Theory and Applications of Cryptographic Techniques. Berlin: Springer, 1999: 223-238.