近些年来，基于位置系统的设备越来越多，从而导致用户的大量位置信息被移动设备获取并利用，从数据挖掘的角度来说，这些数据具有不可估量的价值，但从个人隐私方面来说却恰恰相反，每个人都不希望自己的信息被泄露和利用，从而引发了人们强烈的隐私关注.目前许多文献都提出了隐私保护技术来解决这个问题，概括来说是干扰、抑制和泛化几大类.为了对个人时空数据的隐私进行保护，本文提出了k-泛化的方法.对用户可能出现的点进行范围限定，更好地提高了数据的可用性；对泛化节点的选取要使得用户的安全性最高；考虑了多个敏感节点存在情况下的解决方案，并且出于提高数据效用的目的对多个敏感节点进行了优化.最后通过实验评估了算法的性能并且验证了算法保护个人隐私是有效的.
In recent years, more and more devices based on location system, resulting in a large amount of location information by the mobile device users to access and use, from the perspective of data mining, the data is of immeasurable value, but in terms of personal privacy, people don't want their information to be leaked and used to sparked strong privacy concerns. At present, many papers have proposed privacy protection technology to solve this problem. Generally speaking, there are several categories of interference, suppression and generalization. In order to protect the privacy of personal spatio-temporal data, this paper proposes a method of k-generalization. To limit the scope of the user may appear, improve the availability of data; selection of nodes to generalization so that the user's maximum security; considers multiple sensitive node solutions exist under the condition, and for the purpose of improving the data utility on a number of sensitive nodes are optimized. Finally, the performance of the algorithm is evaluated by experiments, and it is proved that the algorithm is effective to protect personal privacy.
[1] XIAO Y, XIONG L. Protecting Locations with Differential Privacy under Temporal Correlations[C]//The ACM Sigsac Conference on Computer and Communications Security. New York:ACM, 2014:1298-1309.
[2] GEDIK B, LIU L. Protecting location privacy with personalized k-anonymity:Architecture and algorithms[J]. IEEE Transactions on Mobile Computing, 2008, 7(1):1-18.
[3] CICEK A E, NERGIZ M E, SAYGIN Y. Ensuring location diversity in privacy-preserving spatio-temporal data publishing[J]. The VLDB Journal, 2014, 23(4):609-625.
[4] HUNDEPOOL A J, WILLENBORG L C R J. Mu-and tau-argus:Software for statistical disclosure control[J].
[5] SAMARATI P. Protecting respondent's identities in microdata release[J]. IEEE Trans Knowl Data Eng, 2001, 13(6):1010-1027.
[6] YU T, JAJODIA S. Secure Data Management in Decentralized Systems[M]. New York:Springer, 2007.
[7] 田秀霞, 王晓玲, 高明, 等. 数据库服务-安全与隐私保护[J]. 软件学报, 2010(5):991-1006.
[8] ABUL O, BONCHI F, NANNI M. Never Walk Alone:Uncertainty for Anonymity in Moving Objects Databases[C]//IEEE, International Conference on Data Engineering.[S.l.]:IEEE Computer Society, 2008:376-385.
[9] ATZORI M, ATZORI M, SAYGIN Y. Towards trajectory anonymization:A generalization-based approach[C]//Sigspatial ACM Gis 2008 International Workshop on Security and Privacy in Gis and Lbs. New York:ACM, 2008:52-61.
[10] SWEENEY L. K-anonymity:A model for protecting privacy[J]. International Journal on Uncertainty, Fuzziness and Knowledge-Based Systems, 2002, 10(5):557-570.
[11] MACHANAVAJJHALA A, KIFER D, GEHRKE J. L -diversity:Privacy beyond k -anonymity[J]. Acm Transactions on Knowledge Discovery from Data, 2007, 1(1):3.
[12] LI N H, LI T C, VENKATASUBRAMANIAN S. t-Closeness:Privacy Beyond k-Anonymity and l-Diversity[C]//IEEE, International Conference on Data Engineering.[S.l.]:IEEE, 2007:106-115.
[13] MAO J, SONG Q, JIN C, et al. TSCluWin:Trajectory Stream Clustering over Sliding Window[M]//Database Systems for Advanced Applications. US:Springer, 2016.
[14] ZHANG Z, WANG Y, MAO J,et al. DT-KST:Distributed top-k similarity query on big trajectory streams[J]. 2017:199-214.
[15] WU W, XIAO Y, WANG W, et al. k-symmetry model for identity anonymization in social networks[C]//EDBT 2010, International Conference on Extending Database Technology. Switzerland:DBLP, 2010:111-122.
[16] DWORK C. Differential privacy[J]. Lecture Notes in Computer Science, 2006, 4052(2):1-12.
[17] KELLARIS G, PAPADOPOULOS S, XIAO X, et al. Differentially private event sequences over infinite streams[J]. Proceedings of the Vldb Endowment, 2014, 7(12):1155-1166.
[18] CHEN R, FUNG B C M, DESAI B C, et al. Differentially private transit data publication:a case study on the montreal transportation system[C]//ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York:ACM, 2012:213-221.
[19] CAO Y, YOSHIKAWA M. Differentially private real-time data release over infinite trajectory streams[C]//IEEE International Conference on Mobile Data Management.[S.l.]:IEEE, 2015:68-73.
[20] MIGUEL E ANDRÉS, NICOLAS E BORDENABE, LONSTANTINOS Chatzikokolakis, et al. Geo-indistinguishability:Differential privacy for location-based systems[C]//Proceedings of the 2013 ACM SIGSAC Conference on Computer and Communications security. New York:ACM, 2013:901-914.
中国综合性科技类核心期刊(北大核心)