Classification and Evaluation the Privacy Preserving Data Mining Techniques by using a Data Modification-based Framework

In recent years, the data mining techniques have met a serious challenge due to the increased concerning and worries of the privacy, that is, protecting the privacy of the critical and sensitive data. Different techniques and algorithms have been already presented for Privacy Preserving data mining, which could be classified in three common approaches: Data modification approach, Data sanitization approach and Secure Multi-party Computation approach. This paper presents a Data modification- based Framework for classification and evaluation of the privacy preserving data mining techniques. Based on our framework the techniques are divided into two major groups, namely perturbation approach and anonymization approach. Also in proposed framework, eight functional criteria will be used to analyze and analogically assessment of the techniques in these two major groups. The proposed framework provides a good basis for more accurate comparison of the given techniques to privacy preserving data mining. In addition, this framework allows recognizing the overlapping amount for different approaches and identifying modern approaches in this field.

[1]  H. Sebastian Seung,et al.  Algorithms for Non-negative Matrix Factorization , 2000, NIPS.

[2]  ASHWIN MACHANAVAJJHALA,et al.  L-diversity: privacy beyond k-anonymity , 2006, 22nd International Conference on Data Engineering (ICDE'06).

[3]  S L Warner,et al.  Randomized response: a survey technique for eliminating evasive answer bias. , 1965, Journal of the American Statistical Association.

[4]  Philip S. Yu,et al.  A Condensation Approach to Privacy Preserving Data Mining , 2004, EDBT.

[5]  Charu C. Aggarwal,et al.  On the design and quantification of privacy preserving data mining algorithms , 2001, PODS.

[6]  Jie Wang,et al.  Knowledge and Information Systems REGULAR PAPER , 2006 .

[7]  Latanya Sweeney,et al.  k-Anonymity: A Model for Protecting Privacy , 2002, Int. J. Uncertain. Fuzziness Knowl. Based Syst..

[8]  Philip S. Yu,et al.  A Condensation Approach to Privacy Preserving Data Mining , 2004, EDBT.

[9]  Ninghui Li,et al.  t-Closeness: Privacy Beyond k-Anonymity and l-Diversity , 2007, 2007 IEEE 23rd International Conference on Data Engineering.

[10]  Gene H. Golub,et al.  Matrix computations (3rd ed.) , 1996 .

[11]  Kun Liu,et al.  Random projection-based multiplicative data perturbation for privacy preserving distributed data mining , 2006, IEEE Transactions on Knowledge and Data Engineering.

[12]  W. B. Johnson,et al.  Extensions of Lipschitz mappings into Hilbert space , 1984 .

[13]  Charu C. Aggarwal,et al.  On k-Anonymity and the Curse of Dimensionality , 2005, VLDB.

[14]  Wenliang Du,et al.  Using randomized response techniques for privacy-preserving data mining , 2003, KDD '03.

[15]  Robert H. Halstead,et al.  Matrix Computations , 2011, Encyclopedia of Parallel Computing.

[16]  Pierangela Samarati,et al.  Protecting privacy when disclosing information: k-anonymity and its enforcement through generalization and suppression , 1998 .

[17]  Alexandre V. Evfimievski,et al.  Privacy preserving mining of association rules , 2002, Inf. Syst..

[18]  Qi Wang,et al.  On the privacy preserving properties of random data perturbation techniques , 2003, Third IEEE International Conference on Data Mining.

[19]  Adam Meyerson,et al.  On the complexity of optimal K-anonymity , 2004, PODS.

[20]  Jayant R. Haritsa,et al.  Maintaining Data Privacy in Association Rule Mining , 2002, VLDB.

[21]  Jie Wang,et al.  NNMF-Based Factorization Techniques for High-Accuracy Privacy Protection on Non-negative-valued Datasets , 2006, Sixth IEEE International Conference on Data Mining - Workshops (ICDMW'06).

[22]  Keke Chen,et al.  Towards Attack-Resilient Geometric Data Perturbation , 2007, SDM.

[23]  Keke Chen,et al.  Privacy preserving data classification with rotation perturbation , 2005, Fifth IEEE International Conference on Data Mining (ICDM'05).

[24]  Yehuda Lindell,et al.  Privacy Preserving Data Mining , 2002, Journal of Cryptology.