广东工业大学 计算机学院,广东,广州,510006
李杨(1980–) ,女,教授,博士,主要研究方向为多智能体、差分隐私保护
周莹(1998–),女,硕士研究生,主要研究方向为差分隐私保护,E-mail:joying314@163.com
纸质出版:2023
移动端阅览
李杨, 周莹. 基于方向控制的差分隐私轨迹数据发布方法[J]. 广东工业大学学报, 2023, 40(5): 56-63.
Li Yang, Zhou Ying. Differential Privacy Trajectory Data Publishing Based on Orientation Control[J]. 2023, 40(5): 56-63.
李杨, 周莹. 基于方向控制的差分隐私轨迹数据发布方法[J]. 广东工业大学学报, 2023, 40(5): 56-63. DOI: 10.12052/gdutxb.220170.
Li Yang, Zhou Ying. Differential Privacy Trajectory Data Publishing Based on Orientation Control[J]. 2023, 40(5): 56-63. DOI: 10.12052/gdutxb.220170.
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。
With the continuous expansion of differential privacy and its applications
its application in the privacy protection field of trajectory data release has received extensive attention. However
most existing research methods use the Kmeans to cluster the trajectory
which cannot guarantee the final convergence due to the fact that the trajectory datasets under differential privacy constraints are usually disturbed by noise. To addrss this
this paper proposes an orientation control-based differential privacy-preserving trajectory data publishing method. Firstly
a trajectory generalization algorithm based on SKmeans|| clustering is proposed
which updates the centroid via a direction control mechanism during iterative process of clustering
and designs a scoring function in the index mechanism to control the convergence of the centroid
such that the quality of high dimensional data clustering can be improved. Secondly
a trajectory data publishing algorithm based on bounded noise mechanism is designed
which improves the availability of trajectory data after publishing. Meanwhile
the bounded noise mechanism ensures the true count of the hidden trajectory. Finally
the effectiveness of the method proposed in this paper is evaluated by experiments.
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.
SWEENEY L. k-anonymity: a model for protecting privacy [J]. International Journal of Uncertainty, Fuzziness and Knowledge Based Systems, 2002, 10(5): 557-570.
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.
DWORK C. Differential privacy: a survey of results[C]//International Conference on Theory and Applications of Models of Computation. Heidelberg: Springer, 2008: 1-19.
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.
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.
ZHAO X, DONG Y, PI D. Novel trajectory data publishing method under differential privacy [J]. Expert Systems with Applications, 2019, 138: 112791.
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.
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.
GENG Q, VISWANATH P. The optimal noise-adding mechanism in differential privacy [J]. IEEE Transactions on Information Theory, 2015, 62(2): 925-951.
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.
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.
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.
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.
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.
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.
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.
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.
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.
0
浏览量
0
下载量
0
CSCD
关联资源
相关文章
相关作者
相关机构
京公网安备11010802024621