Hierarchical differential privacy hybrid decomposition algorithm for location big data

The biggest feature of the era of big data is that people can easily generate, access, and make use of massive data resources. As one of the most important and popular kind of big data, location big data and its application technology provide users with convenient services. However, improper collection, analysis and publishing of location big data also brings huge crisis of personal privacy disclosure. Spatial decomposition is one of the effective ways to achieve the statistics publication of location big data. In order to make full use of the redundant characteristics of location big data in spatial and temporal distribution, a hierarchical differential privacy hybrid decomposition algorithm is proposed in this paper. In the first layer of decomposition, an adaptive density grid structure is used to cluster the location big data, which not only reduces the uniform assumption errors but also avoids noise errors caused by large number of empty nodes. In order to guide the reasonable decomposition for skewed grids in the second layer, a heuristic quad-tree decomposition algorithm based on regional uniformity is designed, which solved the difficult problem for determining stop condition of the top-down decomposition of two-dimensional space. Comparative experiments show that the hierarchical differential privacy hybrid decomposition algorithm proposed in this paper has good effect in improving the accuracy of regional counting queries. The proposed algorithm has low computational complexity and obvious advantages in the publishing environment of big data.

[1]  K. Tracy,et al.  "Good" and "Bad" Criticism: A Descriptive Analysis. , 1987 .

[2]  Cynthia Dwork,et al.  Differential Privacy , 2006, ICALP.

[3]  Meng Xiaofeng,et al.  Big Data Privacy Management , 2015 .

[4]  Beng Chin Ooi,et al.  From Big Data to Data Science: A Multi-disciplinary Perspective , 2014, Big Data Res..

[5]  Feng Deng,et al.  Big Data Security and Privacy Protection , 2014 .

[6]  Xing Xie,et al.  PrivTree: A Differentially Private Algorithm for Hierarchical Decompositions , 2016, SIGMOD Conference.

[7]  Frank McSherry,et al.  Privacy integrated queries: an extensible platform for privacy-preserving data analysis , 2009, SIGMOD Conference.

[8]  Jubilant J. Kizhakkethottam,et al.  Challenges with big data mining: A review , 2015, 2015 International Conference on Soft-Computing and Networks Security (ICSNS).

[9]  Margaret Martonosi,et al.  DP-WHERE: Differentially private modeling of human mobility , 2013, 2013 IEEE International Conference on Big Data.

[10]  Elisa Bertino,et al.  Private record matching using differential privacy , 2010, EDBT '10.

[11]  Frank Dürr,et al.  A classification of location privacy attacks and approaches , 2012, Personal and Ubiquitous Computing.

[12]  Dan Wang,et al.  Special issue on big data networking-challenges and applications , 2015, Journal of Communications and Networks.

[13]  Ninghui Li,et al.  Differentially private grids for geospatial data , 2012, 2013 IEEE 29th International Conference on Data Engineering (ICDE).

[14]  Wang Ju.  A radar signal sorting algorithm based on dynamic grid density clustering , 2013 .

[15]  Kunal Talwar,et al.  Mechanism Design via Differential Privacy , 2007, 48th Annual IEEE Symposium on Foundations of Computer Science (FOCS'07).

[16]  Nikos Mamoulis,et al.  Local Suppression and Splitting Techniques for Privacy Preserving Publication of Trajectories , 2017, IEEE Transactions on Knowledge and Data Engineering.

[17]  Cynthia Dwork,et al.  Calibrating Noise to Sensitivity in Private Data Analysis , 2006, TCC.

[18]  Divesh Srivastava,et al.  Differentially Private Spatial Decompositions , 2011, 2012 IEEE 28th International Conference on Data Engineering.

[19]  Li Aiping,et al.  Privacy preservation in big data: a survey , 2016 .

[20]  Cheng Xueqi,et al.  Personal Privacy Protection in the Era of Big Data , 2015 .

[21]  Imene Guellil,et al.  Social big data mining: A survey focused on opinion mining and sentiments analysis , 2015, 2015 12th International Symposium on Programming and Systems (ISPS).