Ratio Utility and Cost Analysis for Privacy Preserving Subspace Projection

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Al, Mert; Wan, Shibiao; Kung, Sun-Yuan;
  • Subject: Statistics - Machine Learning | Computer Science - Learning

With a rapidly increasing number of devices connected to the internet, big data has been applied to various domains of human life. Nevertheless, it has also opened new venues for breaching users' privacy. Hence it is highly required to develop techniques that enable dat... View more
  • References (18)
    18 references, page 1 of 2

    [1] Weidong Shi, Jun Yang, Yifei Jiang, Feng Yang, and Yingen Xiong, “Senguard: Passive user identification on smartphones using multiple sensors,” in 2011 IEEE 7th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob). 2011, pp. 141-148, IEEE.

    [2] Delphine Christin, Andreas Reinhardt, Salil S. Kanhere, and Matthias Hollick, “A survey on privacy in mobile participatory sensing applications,” Journal of Systems and Software, vol. 84, no. 11, pp. 1928-1946, 2011.

    [3] Keng Pei Lin and Ming Syan Chen, “On the design and analysis of the privacy-preserving svm classifier,” Knowledge and Data Engineering, IEEE Transactions on, vol. 23, no. 11, pp. 1704-1717, 2011.

    [4] Rakesh Agrawal and Ramakrishnan Srikant, “Privacypreserving data mining,” in ACM Sigmod Record. 2000, vol. 29, pp. 439-450, ACM.

    [5] Hillol Kargupta, Souptik Datta, Qi Wang, and Krishnamoorthy Sivakumar, “On the privacy preserving properties of random data perturbation techniques,” in Data Mining, 2003. ICDM 2003. Third IEEE International Conference on. 2003, pp. 99- 106, IEEE.

    [6] Kun Liu, Hillol Kargupta, and Jessica Ryan, “Random projection-based multiplicative data perturbation for privacy preserving distributed data mining,” IEEE Transactions on Knowledge and Data Engineering, vol. 18, no. 1, pp. 92-106, 2006.

    [7] Bin Liu, Yurong Jiang, Fei Sha, and Ramesh Govindan, “Cloud-enabled privacy-preserving collaborative learning for mobile sensing,” in Proceedings of the 10th ACM Conference on Embedded Network Sensor Systems. 2012, pp. 57-70, ACM.

    [8] Sun-Yuan Kung, “Discriminant component analysis for privacy protection and visualization of big data,” Multimedia Tools and Applications, pp. 1-36, 2015.

    [9] Sun-Yuan Kung, “Compressive privacy: From information/estimation theory to machine learning,” to appear on IEEE Signal Processing Magazine, 2016.

    [10] Sun-Yuan Kung, Kernel methods and machine learning, Cambridge University Press, 2014.

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