Privacy Preserving in the Publication of Large-Scale Trajectory Databases

详细信息    查看全文

• 关键词：Privacy preserving ; Large ; scale databases ; Trajectory data publishing ; Segment clustering
• 刊名：Lecture Notes in Computer Science
• 出版年：2016
• 出版时间：2016
• 年：2016
• 卷：9784
• 期：1
• 页码：367-376
• 全文大小：477 KB
• 参考文献：1.Elahe, G.K., Mahdi, A., Fatemeh, D.: PPTD: preserving personalized privacy in trajectory data publishing by sensitive attribute generalization and trajectory local suppression. Knowl.-Based Syst. 94, 43–59 (2016)CrossRef
  2.Huo, Z., Meng, X.F.: A survey of trajectory privacy preserving techniques. Chin. J. Comput. 34(10), 1820–1830 (2011)CrossRef
  3.Chen, R., Xiao, Q., Zhang, Y.: Differentially private high-dimensional data publication via sampling-based inference. In: 21st ACM SIGKDD Conference on Knowledge Discovery and Data Mining (SIGKDD), pp. 129–138. ACM Press, Sydney (2015)
  4.Parmar, S., Gupta, M.P.: A comparative study and literature survey on privacy preserving data mining techniques. Int. J. Comput. Sci. Mob. Comput. 4(4), 480–486 (2015)
  5.Zhang, L., Cormode, G., Procopiuc, C.M., Srivastava, D.: PrivBayes: private data release via Bayesian networks. In: ACM SIGMOD International Conference on Management of Data, pp. 1423–1434. ACM Press, Snowbird (2014)
  6.Gidofalvi, G., Huang, X., Pedersen, T.B: Privacy-preserving data mining on moving object trajectories. In: International Conference on Mobile Data Management, pp. 60–68. IEEE Press, Mannheim (2007)
  7.Yarovoy, R., Bonchi, F., Lakshmanan, L.V.S.: Anonymizing moving objects: how to hide a MOB in a crowd. In: 12th International Conference on Extending Database Technology, pp. 72–83. ACM Press, Saint Petersburg (2009)
  8.Hoh, B., Gruteser, M., Xiong, H.: Achieving guaranteed anonymity in GPS Traces via uncertainty-aware path cloaking. IEEE Trans. Mob. Comput. 9(8), 1089–1107 (2010)CrossRef
  9.Nergiz, M.E., Atzori, M., Saygin, Y.: Towards trajectory anonymization: a generalization-based approach. In: SIGSPATIAL ACM GIS 2008 International Workshop on Security and Privacy in GIS and LBS, pp. 52–61. ACM Press, Irvine (2008)
  10.Abul, O., Bonchi, F., Nanni, M.: Anonymization of moving objects databases by clustering and perturbation. Inf. Syst. 35(8), 884–910 (2010)CrossRef
  11.Sweeney, L.: k-anonymity: a model for protecting privacy. Int. J. Uncertainty Fuzziness Knowl.-Based Syst. 10(5), 557–570 (2002)MathSciNet CrossRef MATH
  12.Arunkuma, S., Srivatsac, M., Rajarajan, M.: A review paper on preserving privacy in mobile environments. J. Netw. Comput. Appl. 53, 74–90 (2015)CrossRef
  13.Abul, O., Bonchim, F., Nanni, M.: Never walk alone: uncertainty for anonymity in moving objects databases. In: 24th IEEE International Conference on Data Engineering (ICDE 2008), pp. 376–385. IEEE Press, Cancún (2008)
  14.Khalil, A.H., Benjamin, C.M.F., William, K.C.: Privacy-preserving trajectory stream publishing. Data Knowl. Eng. 94, 89–109 (2014)CrossRef
  15.Sashi, G.R., Dan, L., Wei, J., Ali, H., Rui, Z.: Traffic information publication with privacy preservation. ACM Trans. Intell. Syst. Technol. 5(3), 1–26 (2014)CrossRef
  16.Grunwald, P.D., Myung, I.J., Pitt, M.A.: Advances in Minimum Description Length: Theory and Applications, pp. 20–28. MIT Press, Cambridge (2005)
  17.Brinkhoff, T.: Generating traffic data. IEEE Data Eng. Bull. 26(2), 19–25 (2003)
  
• 作者单位：Fengyun Li (18)
  Fuxiang Gao (18)
  Lan Yao (18)
  Yu Pan (18)
  
  18. School of Computer Science and Engineering, Northeastern University, Shenyang, 110819, People’s Republic of China
  
• 丛书名：Big Data Computing and Communications
• ISBN：978-3-319-42553-5
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Computer Communication Networks
  Software Engineering
  Data Encryption
  Database Management
  Computation by Abstract Devices
  Algorithm Analysis and Problem Complexity
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1611-3349
• 卷排序：9784

文摘

In recent years, preserving individual privacy when publishing trajectory data receives increasing attention. However, the existing trajectory data privacy preserving techniques cannot resolve the anonymous issues of large-scale trajectory databases. In traditional clustering constraint based trajectory privacy preserving algorithms, the anonymous groups lack of diversity and they cannot effectively prevent re-clustering attacks against the characteristics of publishing data. In this thesis, a segment clustering based privacy preserving algorithm is proposed. Firstly, the original database is divided into blocks and each block is treated as a separate database. Then, the trajectories in each block are partitioned into segments based on the minimum description length principle. Lastly, these segments are anonymized with cluster-constraint strategy. Experimental results show that the proposed algorithm can improve the safety and have good performance in data quality and anonymous efficiency.