A Novel Approach for Personalized Privacy Preserving Data Publishing with Multiple Sensitive Attributes

 
 
 
  • Abstract
  • Keywords
  • References
  • PDF
  • Abstract


    The Personalized Privacy has drawn a lot of attention from diverse magnitudes of the public and various functional units like bureau of statistics, and hospitals. A large number of data publishing models and methods have been proposed and most of them focused on single sensitive attribute. A few research papers marked the need for preserving privacy of data consisting of multiple sensitive attributes. Applying the existing methods such as k-anonymity, l-diversity directly for publishing multiple sensitive attributes would minimize the utility of the data. Moreover, personalization has not been studied in this dimension. In this paper, we present a publishing model that manages personalization for publishing data with multiple sensitive attributes. The model uses slicing technique supported by deterministic anonymization for quasi identifiers; generalization for categorical sensitive attributes; and fuzzy approach for numerical sensitive attributes based on diversity. We cap the belief of an adversary inferring a sensitive value in a published data set to as high as that of an inference based on public knowledge. The experiments were carried out on census dataset and synthetic datasets. The results ensure that the privacy is being safeguarded without any compromise on the utility of the data.

     

     


  • Keywords


    Anonymity; Categorical Sensitive attributes; Data Publishing; Diversity; Numerical Sensitive Attributes ; Personalized Privacy.

  • References


      [1] L. Sweeney, “k-anonymity: A model for protecting privacy”, International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, Vol.10, No.5, (2002), pp. 557–570, http://dx.doi.org/10.1142/S0218488502001648.

      [2] K. Stokes and V. Torra, “n-confusion: A generalization of k-anonymity”, Proceedings of the 2012 Joint EDBT/ICDT Workshops, ACM, (2012), pp. 211–215, https://dl.acm.org/citation.cfm?id=2320824.

      [3] J. Liu and K.Wang, “Enforcing vocabulary k-anonymity by semantic similarity based clustering”, Proceedings of the 2010 IEEE 10th International Conference on Data Mining, (2010), pp. 899–904, http://dx.doi.org/10.1109/ICDM.2010.59.

      [4] C. Wang, L. Liu, and L. Gao, “Research on k-anonymity algorithm in privacy protection”, Advanced Materials Research, Vols. 756-759, (2013), pp. 3471–3475, https://doi.org/10.4028/www.scientific.net/AMR.756-759.3471.

      [5] A. Machanavajjhala, D. Kifer, J. Gehrke, and M. Venkitasubramaniam, “l-diversity: Privacy beyond k-anonymity”, ACM Transactions on Knowledge Discovery from Data, Vol.1, No. 1, (2007), pp. 1–47, http://dx.doi.org/10.1145/1217299.1217302.

      [6] N. Li, T. Li, and S. Venkatasubramanian, “t -closeness: Privacy beyond k-anonymity and l-diversity”, Proceedings of the IEEE 23rd International Conference on Data Engineering (2007), pp. 106–115, http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4221659&isnumber=4221635.

      [7] D. J Martin, D. Kifer, A. Machanavajjhala, J. Gehrke, and J. Y Halpern, “Worst-case background knowledge for privacy-preserving data publishing”, Proceedings of the IEEE 23rd International Conference on Data Engineering, (2007), pp. 126–135, http://doi.ieeecomputersociety.org/10.1109/ICDE.2007.367858.

      [8] M. E. Nergiz, M. Atzori, and C. Clifton, “Hiding the presence of individuals from shared databases”, Proceedings of the 2007 ACM International Conference on Management of Data, (2007), pp. 665–676, https://doi.org/10.1145/1247480.1247554.

      [9] X. Xiao and Y. Tao, “Anatomy: Simple and effective privacy preservation”, Proceedings of the 32nd International Conference on Very Large Data Bases, (2006), pp. 139–150, https://dl.acm.org/citation.cfm?id=1164141.

      [10] Ye, Y., Liu, Y., Lv, D., & Feng, J., “Decomposition: Privacy preservation for multiple sensitive attributes”, Database Systems for Advanced Applications, Lecture Notes in Computer Science, Springer, Vol. 5463, (2009), pp. 1-15, https://doi.org/10.1007/978-3-642-00887-0_42.

      [11] Das, D., & Bhattacharyyu, D. K., “Decomposition+: Improving l-diversity for Multiple Sensitive Attributes”, Advances in Computer Science and Information Technology, Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, Springer, Vol. 85, (2012), pp. 1-10, https://doi.org/10.1007/978-3-642-27308-7_44.

      [12] Liu, F., Jia, Y., & Han, W., “A new K-anonymity algorithm towards multiple-sensitive attributes”, Proceedings of the IEEE 12th International Conference on Computer and Information Technology (2012), pp. 768-772, http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6391995&isnumber=6391864.

      [13] Han, J., Luo, F., Lu, J., & Peng, H., “SLOMS: A privacy preserving data publishing methods for multiple sensitive attributes micro data”, Journal of Software, Vol. 8, No. 12, (2013), pp. 3096-3104, https://doi.org/10.4304/jsw.8.12.3096-3104.

      [14] Liu, Q., Shen, H., & Sang, Y., “Privacy-preserving data publishing for multiple numerical sensitive attributes”, Tsinghua Science and Technology, Vol. 20, No. 3, (2015), pp. 246–254, http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7128936&isnumber=7128931.

      [15] Dua, D. and Karra Taniskidou, E., “UCI Machine Learning Repository”, Irvine, CA: University of California, School of Information and Computer Science, (2017) , http://archive.ics.uci.edu/ml.

      [16] Gal, Tamas S., Zhiyuan Chen, Aryya Gangopadhyay, “A privacy protection model for patient data with multiple sensitive attributes”, International Journal of Information Security and Privacy, Vol. 2, No. 3, (2008), pp. 28-44, http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/jisp.2008070103.

      [17] V. Valli Kumari, S. Ram Prasad Reddy, M. Aruna Sowjanya, B. Jhansi Vazram, KVSVN Raju, “A novel approach for privacy preserving publication of data”, Proceedings of the 2008 International Conference on Data Mining, (2008), pp. 506-512, https://dblp.org/rec/bib/conf/dmin/VallikumariRSVR08.

      [18] Yi,T. Shi,M., “Privacy protection method for multiple sensitive attributes based on strong rule”, Mathematical Problems in Engineering (2015), Vol. 2015, pp. 1-14, http://dx.doi.org/10.1155/2015/464731.

      [19] Radha,D Valli Kumari, V., “Bucketize: protecting privacy on multiple numerical sensitive attributes”, Advances in Computational Sciences and Technology, Vol. 10, No. 5, (2017), pp. 991-1008, https://www.ripublication.com/acst17/acstv10n5_32.pdf.

      [20] S. A. Onashoga, B. A. Bamiro, A. T. Akinwale & J. A. Oguntuase , “KC-Slice: A dynamic privacy-preserving data publishing technique for multisensitive attributes”, Information Security Journal: A Global Perspective, Vol 26, No.3, (2017), pp. 121-135, https://doi.org/10.1080/19393555.2017.1319522.


 

View

Download

Article ID: 13296
 
DOI: 10.14419/ijet.v7i2.20.13296




Copyright © 2012-2015 Science Publishing Corporation Inc. All rights reserved.