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CN 34-1054/N

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A privacy preserving measurement for query under k-anonymity mechanism

  • College of Computer Science and Engineering, Anhui University of Science and Technology, Huainan 232001, China

Cite this:
https://doi.org/10.3969/j.issn.0253-2778.2018.06.010
  • Received Date: 05 September 2017
  • Accepted Date: 10 April 2018
  • Rev Recd Date: 10 April 2018
  • Publish Date: 30 June 2018
    Abstract

  • Abstract

    A query privacy measurement was proposed under the k-anonymity mechanism. The method was based on information entropy and logarithmic function. First, a framework for query privacy under the k-anonymity mechanism is established, which contains four roles and four operations provides a formal description for privacy measurement. Then, two quantitative methods of background knowledge are introduced. For the second step, user attribute discretization values will be calculated as a probability expression of background knowledge, affects the accuracy of the probability expression. The value of each user attribute after discretization was proposed as the index of the array to calculate the relevant quantities, the index of the array being generated by the relevance of the particular query and the attributes of the user, so as to further obtain the probability of the user issuing the particular query, thus avoiding the influence of discretized values of user attributes on the quantification results. Finally, a query privacy measurement is proposed. The experimental results show that the method can effectively measure the level of protection of the query privacy protection algorithm under k-anonymity mechanism.

    Abstract

    A query privacy measurement was proposed under the k-anonymity mechanism. The method was based on information entropy and logarithmic function. First, a framework for query privacy under the k-anonymity mechanism is established, which contains four roles and four operations provides a formal description for privacy measurement. Then, two quantitative methods of background knowledge are introduced. For the second step, user attribute discretization values will be calculated as a probability expression of background knowledge, affects the accuracy of the probability expression. The value of each user attribute after discretization was proposed as the index of the array to calculate the relevant quantities, the index of the array being generated by the relevance of the particular query and the attributes of the user, so as to further obtain the probability of the user issuing the particular query, thus avoiding the influence of discretized values of user attributes on the quantification results. Finally, a query privacy measurement is proposed. The experimental results show that the method can effectively measure the level of protection of the query privacy protection algorithm under k-anonymity mechanism.
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    • References(21)
  • [1]
    崔宁宁, 杨晓春, 王斌, 等. 移动k-支配最近邻查询验证研究[J/OL]. 计算机学报,2017, 40: 113[2017-08-05]. http://kns.cnki.net/kcms/detail/11.1826.TP.20170728.1258.032.html.
    CUI Ningning, YANG Xiaochun, WANG Bin, et al. Research on authentication of moving k-dominant NN queries[J/OL]. Chinese Journal of Computers, 2017, 40: 113[2017-08-05].http://kns.cnki.net/kcms/detail/11.1826.TP.20170728.1258.032.html.
    [2]
    ARTAIL H, ABBANI N. A pseudonym management system to achieve anonymity in vehicular Ad Hoc networks[J].IEEE Transactions on Dependable and Secure Computing, 2016, 13(1):106-119.
    [3]
    CHEN D, LI H, ZHOU S. CSEP: Circular shifting encryption protocols for location privacy protection[C]// IEEE/ACIS International Conference on Computer and Information Science. Piscataway, NY, USA: IEEE Press, 2017:45-50.
    [4]
    BASERI Y, HAFID A, CHERKAOUI S. K-anonymous location-based fine-grained access control for mobile cloud[C]// Consumer Communications and NETWORKING Conference. Piscataway, NY, USA: IEEE Press, 2016:720-725.
    [5]
    万盛, 李凤华, 牛犇, 等. 位置隐私保护技术研究进展[J]. 通信学报, 2016, 37(12):124-141.
    WAN Sheng, LI Fenghua, NIU Ben, et al. Research progress on location privacy-preserving techniques[J]. Journal on Communications, 2016, 37(12):124-141.
    [6]
    张学军, 桂小林, 冯志超,等. 位置服务中的查询隐私度量框架研究[J]. 西安交通大学学报, 2014, 48(2):8-13.
    ZHANG Xuejun, GUI Xiaolin, FENG Zhichao, et al. A quantifying framework of query privacy in location-based service[J]. Journal of Xi'an Jiaotong University, 2014, 48(2):8-13.
    [7]
    彭长根, 丁红发, 朱义杰,等. 隐私保护的信息熵模型及其度量方法[J]. 软件学报, 2016, 27(8):1891-1903.
    PENG Changgen, DING Hongfa, ZHU Yijie, et al. Information Entropy Models and Privacy Metrics Methods for Privacy Protection[J]. Journal of Software, 2016, 27(8):1891-1903.
    [8]
    史敏仪. 面向位置服务的轨迹隐私保护技术研究[D]. 南京：南京邮电大学, 2014.
    [9]
    MOKBEL M F, CHOW C Y, AREF W G. The new casper: A privacy-aware location-based database server[C]// IEEE International Conference on Data Engineering. Turkey: IEEE Computer Society, 2007:1499-1500.
    [10]
    王玲玲, 马春光, 刘国柱. 基于位置服务的隐私保护机制度量研究综述[J]. 计算机应用研究, 2017, 34(3):647-652.
    WANG Lingling, MA Chunguang, LIU Guozhu. Survey on metrics for location-based privacy protection mechanisms[J]. Application Ｒesearch of Computers, 2017, 34(3):647-652.
    [11]
    SHOKRI R, THEODORAKOPOULOS G, TRONCOSO C, et al. Protecting location privacy: Optimal strategy against localization attacks[C]// Proceedings of the 2012 ACM Conference on Computer and Communications Security. New York, NY, USA : ACM, 2012:617-627.
    [12]
    THEODORAKOPOULOS G, SHOKRI R, TRONCOSO C, et al. Prolonging the hide-and-seek game: Optimal trajectory privacy for location-based services[C]// Proceedings of the 13th Workshop on Privacy in the Electronic Society. New York, NY, USA : ACM, 2014:73-82.
    [13]
    KELLY D J, RAINES R A, GRIMAILA M R, et al. A survey of state-of-the-art in anonymity metrics[C]// ACM Workshop on Network Data Anonymization. New York, NY, USA : ACM, 2008:31-40.
    [14]
    林欣, 李善平, 杨朝晖. LBS中连续查询攻击算法及匿名性度量[J]. 软件学报, 2009, 20(4):1058-1068.
    LIN Xin, LI Shanping, YANG Zhaohui. Attacking algorithms against continuous queries in LBS and anonymity measurement[J]. Journal of Software, 2009, 20(4):1058-1068.
    [15]
    HUANG L P, YAMANE H, MATSUURA K. Silent cascade: Enhancing location privacy without communication Qos degradation[C]// International Conference on Security in Pervasive Computing. Berlin: Springer-Verlag, 2006: 165-180.
    [16]
    HOH B, GRUTESER M, XIONG H, et al. Preserving privacy in GPS traces via uncertainty-aware path cloaking[C]// ACM Conference on Computer and Communications Security. New York, NY, USA: ACM, 2007: 161-171
    [17]
    CHEN X, PANG J. Measuring query privacy in location-based services[C]// Proceedings of the second ACM conference on Data and Application Security and Privacy. New York, NY, USA: ACM, 2012: 49-60.
    [18]
    FREUDIGER J, SHOKRI R, HUBAUX J P. On the optimal placement of mix zones[C]//Proceedings of the 9th International Symposium on Privacy Enhancing Technologies. Berlin: Springer-Verlag, 2009: 216-234.
    [19]
    ZHANG X J, GUI X L, TIAN F. Privacy quantification model based on the bayes conditional risk in location-based services[J]. TsingHua Science and Technology, 2014, 19(5): 452-462.
    [20]
    DU W, TENG Z, ZHU Z. Privacy-maxENT: Integrating background knowledge in privacy quantification[C]// Proceedings of the 2008 ACM SIGMOD international conference on Management of data. New York, NY, USA: ACM, 2008:459-472.
    [21]
    王彩梅, 郭亚军, 郭艳华. 位置服务中用户轨迹的隐私度量[J]. 软件学报, 2012, 23(2):352-360.
    WANG Caimei, GUO Yajun, GUO Yanhua. Privacy metric for user’s trajectory in location-based services[J]. Journal of Software, 2012, 23(2):352-360.
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    [1]
    崔宁宁, 杨晓春, 王斌, 等. 移动k-支配最近邻查询验证研究[J/OL]. 计算机学报,2017, 40: 113[2017-08-05]. http://kns.cnki.net/kcms/detail/11.1826.TP.20170728.1258.032.html.
    CUI Ningning, YANG Xiaochun, WANG Bin, et al. Research on authentication of moving k-dominant NN queries[J/OL]. Chinese Journal of Computers, 2017, 40: 113[2017-08-05].http://kns.cnki.net/kcms/detail/11.1826.TP.20170728.1258.032.html.
    [2]
    ARTAIL H, ABBANI N. A pseudonym management system to achieve anonymity in vehicular Ad Hoc networks[J].IEEE Transactions on Dependable and Secure Computing, 2016, 13(1):106-119.
    [3]
    CHEN D, LI H, ZHOU S. CSEP: Circular shifting encryption protocols for location privacy protection[C]// IEEE/ACIS International Conference on Computer and Information Science. Piscataway, NY, USA: IEEE Press, 2017:45-50.
    [4]
    BASERI Y, HAFID A, CHERKAOUI S. K-anonymous location-based fine-grained access control for mobile cloud[C]// Consumer Communications and NETWORKING Conference. Piscataway, NY, USA: IEEE Press, 2016:720-725.
    [5]
    万盛, 李凤华, 牛犇, 等. 位置隐私保护技术研究进展[J]. 通信学报, 2016, 37(12):124-141.
    WAN Sheng, LI Fenghua, NIU Ben, et al. Research progress on location privacy-preserving techniques[J]. Journal on Communications, 2016, 37(12):124-141.
    [6]
    张学军, 桂小林, 冯志超,等. 位置服务中的查询隐私度量框架研究[J]. 西安交通大学学报, 2014, 48(2):8-13.
    ZHANG Xuejun, GUI Xiaolin, FENG Zhichao, et al. A quantifying framework of query privacy in location-based service[J]. Journal of Xi'an Jiaotong University, 2014, 48(2):8-13.
    [7]
    彭长根, 丁红发, 朱义杰,等. 隐私保护的信息熵模型及其度量方法[J]. 软件学报, 2016, 27(8):1891-1903.
    PENG Changgen, DING Hongfa, ZHU Yijie, et al. Information Entropy Models and Privacy Metrics Methods for Privacy Protection[J]. Journal of Software, 2016, 27(8):1891-1903.
    [8]
    史敏仪. 面向位置服务的轨迹隐私保护技术研究[D]. 南京：南京邮电大学, 2014.
    [9]
    MOKBEL M F, CHOW C Y, AREF W G. The new casper: A privacy-aware location-based database server[C]// IEEE International Conference on Data Engineering. Turkey: IEEE Computer Society, 2007:1499-1500.
    [10]
    王玲玲, 马春光, 刘国柱. 基于位置服务的隐私保护机制度量研究综述[J]. 计算机应用研究, 2017, 34(3):647-652.
    WANG Lingling, MA Chunguang, LIU Guozhu. Survey on metrics for location-based privacy protection mechanisms[J]. Application Ｒesearch of Computers, 2017, 34(3):647-652.
    [11]
    SHOKRI R, THEODORAKOPOULOS G, TRONCOSO C, et al. Protecting location privacy: Optimal strategy against localization attacks[C]// Proceedings of the 2012 ACM Conference on Computer and Communications Security. New York, NY, USA : ACM, 2012:617-627.
    [12]
    THEODORAKOPOULOS G, SHOKRI R, TRONCOSO C, et al. Prolonging the hide-and-seek game: Optimal trajectory privacy for location-based services[C]// Proceedings of the 13th Workshop on Privacy in the Electronic Society. New York, NY, USA : ACM, 2014:73-82.
    [13]
    KELLY D J, RAINES R A, GRIMAILA M R, et al. A survey of state-of-the-art in anonymity metrics[C]// ACM Workshop on Network Data Anonymization. New York, NY, USA : ACM, 2008:31-40.
    [14]
    林欣, 李善平, 杨朝晖. LBS中连续查询攻击算法及匿名性度量[J]. 软件学报, 2009, 20(4):1058-1068.
    LIN Xin, LI Shanping, YANG Zhaohui. Attacking algorithms against continuous queries in LBS and anonymity measurement[J]. Journal of Software, 2009, 20(4):1058-1068.
    [15]
    HUANG L P, YAMANE H, MATSUURA K. Silent cascade: Enhancing location privacy without communication Qos degradation[C]// International Conference on Security in Pervasive Computing. Berlin: Springer-Verlag, 2006: 165-180.
    [16]
    HOH B, GRUTESER M, XIONG H, et al. Preserving privacy in GPS traces via uncertainty-aware path cloaking[C]// ACM Conference on Computer and Communications Security. New York, NY, USA: ACM, 2007: 161-171
    [17]
    CHEN X, PANG J. Measuring query privacy in location-based services[C]// Proceedings of the second ACM conference on Data and Application Security and Privacy. New York, NY, USA: ACM, 2012: 49-60.
    [18]
    FREUDIGER J, SHOKRI R, HUBAUX J P. On the optimal placement of mix zones[C]//Proceedings of the 9th International Symposium on Privacy Enhancing Technologies. Berlin: Springer-Verlag, 2009: 216-234.
    [19]
    ZHANG X J, GUI X L, TIAN F. Privacy quantification model based on the bayes conditional risk in location-based services[J]. TsingHua Science and Technology, 2014, 19(5): 452-462.
    [20]
    DU W, TENG Z, ZHU Z. Privacy-maxENT: Integrating background knowledge in privacy quantification[C]// Proceedings of the 2008 ACM SIGMOD international conference on Management of data. New York, NY, USA: ACM, 2008:459-472.
    [21]
    王彩梅, 郭亚军, 郭艳华. 位置服务中用户轨迹的隐私度量[J]. 软件学报, 2012, 23(2):352-360.
    WANG Caimei, GUO Yajun, GUO Yanhua. Privacy metric for user’s trajectory in location-based services[J]. Journal of Software, 2012, 23(2):352-360.
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