广东工业大学学报 ›› 2023, Vol. 40 ›› Issue (05): 56-63.doi: 10.12052/gdutxb.220170
• 计算机科学与技术 • 上一篇
李杨, 周莹
Li Yang, Zhou Ying
摘要: 随着差分隐私研究及其应用的不断拓展,其在轨迹数据发布的隐私保护领域应用受到了广泛关注,现有研究方法大多采用Kmeans聚类方法对轨迹进行聚类划分,但由于差分隐私约束下的轨迹数据集受到噪声的扰动,导致现有的聚类方法无法保证最后的收敛效果。本文提出了一种基于方向控制的差分隐私保护轨迹数据发布方法。首先,提出了基于SKmeans||聚类的轨迹泛化算法,在聚类迭代过程中针对质心的更新,加入方向控制机制,设计指数机制中的打分函数控制质心的收敛,保证高维数据聚类的质量。其次,设计了一个基于有界阶梯噪声机制的轨迹数据发布算法,其中的有界阶梯噪声机制保证了在隐藏轨迹点真实计数的同时,提高了发布后轨迹数据的可用性。最后,通过实验验证了本文所提出方法的有效性。
中图分类号:
[1] LI J Y, GUO W Z, LI X Y, et al. Privacy-preserving real-time road conditions monitoring scheme based on intelligent traffic [J]. Journal on Communications, 2020, 41(7): 73-83. [2] SWEENEY L. k-anonymity: a model for protecting privacy [J]. International Journal of Uncertainty, Fuzziness and Knowledge Based Systems, 2002, 10(5): 557-570. [3] MACHANAVAJJHALA A, KIFER D, GEHRKE J, et al. l-diversity: privacy beyond k-anonymity [J]. ACM Transactions on Knowledge Discovery from Data (TKDD), 2007, 1(1): 3-14. [4] DWORK C. Differential privacy: a survey of results[C]//International Conference on Theory and Applications of Models of Computation. Heidelberg: Springer, 2008: 1-19. [5] 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]// Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Beijing: ACM, 2012: 213-221. [6] CHEN R, ACS G, CASTELLUCCIA C. Differentially private sequential data publication via variable-length n-grams[C]//Proceedings of the 2012 ACM Conference on Computer and Communications Security. Raleigh: ACM, 2012: 638-649. [7] ZHAO X, DONG Y, PI D. Novel trajectory data publishing method under differential privacy [J]. Expert Systems with Applications, 2019, 138: 112791. [8] HUA J, GAO Y, ZHONG S. Differentially private publication of general time-serial trajectory data[C]//2015 IEEE Conference on Computer Communications (INFOCOM). Hong Kong: IEEE, 2015: 549-557. [9] LI M, ZHU L, ZHANG Z, et al. Achieving differential privacy of trajectory data publishing in participatory sensing [J]. Information Sciences, 2017, 400: 1-13. [10] GENG Q, VISWANATH P. The optimal noise-adding mechanism in differential privacy [J]. IEEE Transactions on Information Theory, 2015, 62(2): 925-951. [11] LI Y, YANG D, HU X. A differential privacy-based privacy-preserving data publishing algorithm for transit smart card data [J]. Transportation Research Part C:Emerging Technologies, 2020, 115: 102634. [12] GURSOY M E, LIU L, TRUEX S, et al. Differentially private and utility preserving publication of trajectory data [J]. IEEE Transactions on Mobile Computing, 2018, 18(10): 2315-2329. [13] NI T, QIAO M, CHEN Z, et al. Utility efficient differentially private K-means clustering based on cluster merging [J]. Neurocomputing, 2021, 424: 205-214. [14] LU Z, SHEN H. Differentially private K-means clustering with convergence guarantee [J]. IEEE Transactions on Dependable and Secure Computing, 2020, 18(4): 1541-1552. [15] LIU Q, YU J, HAN J, et al. Differentially private and utility-aware publication of trajectory data [J]. Expert Systems with Applications, 2021, 180: 115120. [16] HAMALAINEN J, KARKKAINEN T, ROSSI T. Scalable initialization methods for large-scale clustering[EB/OL]. arXiv preprint arXiv: 2007.11937 (2020-07-23)[2022-11-01].https://doi.org/10.48550/arXiv.2007.11937. [17] XU C, ZHU L, LIU Y, et al. DP-LTOD: differential privacy latent trajectory community discovering services over location-based social networks [J]. IEEE Transactions on Services Computing, 2018, 14(4): 1068-1083. [18] 陈思, 付安民, 苏铓, 等. 基于差分隐私的轨迹隐私保护方案[J]. 通信学报, 2021, 42(9): 54-64. CHEN S, FU A M, SU M, et al. Trajectory privacy protection scheme based on differential privacy [J]. Journal on Communications, 2021, 42(9): 54-64. [19] ZHAO X, PI D, CHEN J. Novel trajectory privacy-preserving method based on clustering using differential privacy [J]. Expert Systems with Applications, 2020, 149: 113241. [20] YUAN J, ZHENG Y, ZHANG C, et al. T-drive: driving directions based on taxi trajectories[C]// Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems. California: ACM, 2010: 99-108. |
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