匿名化隐私保护技术性能评估方法研究

信息安全研究 ›› 2019, Vol. 5 ›› Issue (4): 293-297.

匿名化隐私保护技术性能评估方法研究

谷勇浩¹,郭振洋¹,刘威歆²

1. 北京邮电大学计算机学院
2. 北京神州绿盟信息安全科技股份有限公司

收稿日期:2019-04-08 出版日期:2019-04-15 发布日期:2019-04-08
通讯作者: 谷勇浩
作者简介:谷勇浩 1980年生，博士，硕导，主要研究方向：网络安全。 guyonghao@bupt.edu.cn 郭振洋 1992年生，硕士研究生，主要研究方向：网络安全。 583958562@qq.com 刘威歆 1987年生，研究员，主要研究方向：网络安全。

Research on Performance Evaluation Method of Anonymization Privacy Preservation Technologies

Received:2019-04-08 Online:2019-04-15 Published:2019-04-08

摘要/Abstract

摘要： 摘要在物联网应用推广过程中，信息安全与隐私泄露问题越发明显，需要一个完善的物联网信息安全和隐私保护机制.保护物联网数据隐私的匿名化技术种类较多，如何从定量的角度对不同种类的匿名保护技术的性能进行评估是一个重要且有意义的研究方向.在分析比较了基于距离和基于相似度的度量方法基础上，采用信息论中散度的概念来衡量概率分布间的差异.最后，使用加州大学欧文分校(UCI)机器学习数据库中的数据验证所提方法的可行性，实验结果给出3种常用的匿名隐私保护技术在隐私增益与数据可用性关系的对比.

关键词: 隐私保护, 数据可用性, 隐私增益, 相似度, 散度

Abstract: Information security and privacy disclosure become more and more serious in the applications of Internet of things (IoT), which need perfect information security architecture and privacy protection mechanism. There are so many types of anonymization technologies for privacy preserving, but how to evaluate their performance from a quantitative perspective is an important and meaningful research direction. In this paper, based on the analysis and comparison of distance and similarity, several metrics are surveyed. In the following, we use the divergence function to measure the difference between two probability distributions. In the end, we evaluate our method on the Adult dataset from UCI machine learning repository and the result shows the comparison figure of privacy gain and data utility among three commonlyused anonymization privacy preservation technologies.

Key words: privacy preservation, data utility, privacy gain, similarity, divergence

谷勇浩郭振洋刘威歆. 匿名化隐私保护技术性能评估方法研究[J]. 信息安全研究, 2019, 5(4): 293-297.

参考文献

[1] OLESHCHUK V. Internet of things and privacy preserving technologies[C]. Proceedings of the 2009 1st International Conference on Wireless Communication, Vehicular Technology, Information Theory and Aerospace and Electronic Systems Technology. Aalborg, Denmark, 2009:336-340. [2] BROLL G., RUKZIO E., PAOLUCCI M., et al. Perci: pervasive service interaction with the Internet of things [J]. Internet Computing, 2009, 13(6): 74-81. [3]OHKUBO, M, SUZUKI K, KINOSHITA S.RFID privacy issues and technical challenges [J]. Communications of the ACM, 2005, 48(9): 66-71. [4] LI Na, ZHANG Nan, Das SAJAL K, et al. Privacy preservation in wireless sensor networks: A state-of-the-art survey [J]. Ad Hoc Networks, 2009, 7 (8):1501-1514. [5] 赵宝康.无线传感器网络隐私保护关键技术研究[D].长沙：国防科学技术大学博士论文 2009.4. [6] 朱青, 赵桐, 王珊.面向查询服务的数据隐私保护算法[J].计算机学报，2010,33（8）:1315-1323. [7] 钱萍，吴蒙.同态加密隐私保护数据挖掘方法综述[J].计算机应用研究, 2011, 28(5):1614-1617. [8] 顾晶晶, 陈松灿, 庄毅.基于无线传感器网络拓扑结构的物联网定位模型[J].计算机学报，2010, 33(9):1548-1556. [9] ZHANG N, ZHAO W. Privacy-preserving data mining systems [J]. Computer, 2007, 40(4): 52-58. [10] 周水庚，李丰，陶宇飞等.面向数据库应用的隐私保护研究综述[J].计算机学报. 2009,32(5)：847-861. [11] L. Sweeney, "k-anonymity: a model for protecting privacy," International Journal of Uncertainty, Fuzziness, and Knowledge-based Systems, vol. 5, pp.557-570, 2002. [12] A. Machanavajjhala, J. Gehrke, D. Kifer, and M. Venkitasubramaniam. l-diversity: Privacy beyond k-anonymity. In ICDE, pp. 24, 2006. [13] R. C.-W. Wong, J. Li, A. W.-C. Fu, and K. Wang. (α, k)-anonymity: an enhanced k-anonymity model for privacy preserving data publishing. In KDD, pp.754–759, 2006. [14] N. Li, T. Li, and S. Venkatasubramanian. t-closeness: Privacy beyond k-anonymity and l-diversity. In ICDE, pp.106–115, 2007. [15] M. E. Nergiz, M. Atzori, and C. Clifton. Hiding the presence of individuals from shared databases. In SIGMOD, pp.665–676, 2007. [16] Malin B, Airoldi E, Edoho-Eket S, Li Y. Configurable security protocols for multi-party data analysis with malicious participants. Proceedings of the 21st International Conference on Data Engineering (ICDE). Tokyo, Japan, 2005:533-544. [17]Huang Zheng-Li, Du Wen-Liang, Chen Biao. Deriving private information from randomized data. Proceedings of the ACM SIGMOD International Conference on Management of Data (SIGMOD). Baltimore, USA, 2005: 37-48. [18] RIVEST R L, ADLEMAN L, DETROUZOS M L. On Data Banks and Privacy Homomorphism[C]. Foundations of Secure Computation. New York: Academic Press, 1978:169-179. [19]GOLLE P., JAKOBSSON M., JUELS A., SYVERSON P..Universal re-encryption for mixnets[C].Proceedings of the Cryptographers’ Track at the RSA Conference 2004(CT-RSA 2004). Lectures Notes in Computer Science 2964. Berlin: Springer-Verlag, 2004:163-178. [20]Csiszár, I. "Eine informationstheoretische Ungleichung und ihre Anwendung auf den Beweis der Ergodizitat von Markoffschen Ketten". Magyar. Tud. Akad. Mat. Kutato Int. Kozl 8: 85–108, 1963. [21]Morimoto, T. "Markov processes and the H-theorem". J. Phys. Soc. Jap. 18 (3): 328–331. doi:10.1143/JPSJ.18.328,1963. [22]Ali, S. M.; Silvey, S. D. "A general class of coefficients of divergence of one distribution from another". Journal of the Royal Statistical Society, Series B 28 (1): 131–142, 1966. [23]A. Asuncion and D. Newman. UCI machine learning repository, http://archive.ics.uci.edu/ml/datasets/Adult. [24] Divergence from Wikipedia. http://en.wikipedia.org/wiki/Divergence_(statistics).