Data mining is the extraction of vast interesting patterns or knowledge from huge amount of data. The initial idea of privacy-preserving data mining PPDM was to extend traditional data mining techniques to work with the data modified to mask sensitive information. The key issues were how to modify the data and how to recover the data mining result from the modified data. Privacy-preserving data mining considers the problem of running data mining algorithms on confidential data that is not supposed to be revealed even to the party running the algorithm. In contrast, privacy-preserving data publishing (PPDP) may not necessarily be tied to a specific data mining task, and the data mining task may be unknown at the time of data publishing. PPDP studies how to transform raw data into a version that is immunized against privacy attacks but that still supports effective data mining tasks. Privacy-preserving for both data mining (PPDM) and data publishing (PPDP) has become increasingly popular because it allows sharing of privacy sensitive data for analysis purposes. One well studied approach is the k-anonymity model  which in turn led to other models such as confidence bounding, l-diversity, t-closeness, (α,k)-anonymity, etc. In particular, all known mechanisms try to minimize information loss and such an attempt provides a loophole for attacks. The aim of this paper is to present a survey for most of the common attacks techniques for anonymization-based PPDM & PPDP and explain their effects on Data Privacy.
 R. C. Wong, J. Li, A. W. Fu, et a1., “(α,k)-Anonymity: An Enhaned k-Anonymity Model for Privacy-Preserving Data Publishing,” In: Proceedings of the 12th ACM SIGKDD, ACM Press, New York, 2006, pp. 754-759.
 K. LeFevre, D. J. DeWitt and R. Ramakrishnan, “Incognito: Efficient Full-Domain k-Anonymity,” In: Proceedings of the ACM SIGMOD International Conference on Management of Data, Baltimore, June 2005, pp. 49-60.
 K. LeFevre, D. J. DeWitt and R. Ramakrishnan, “Workload-Aware Anonymization,” Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Philadelphia, August 2006, pp. 277-286. doi:10.1145/1150402.1150435
 “Business for Social Responsibility,” BSR Report on Privacy, 1999. http://www.bsr.org/
 B. Krishnamurthy, “Privacy vs. Security in the Aftermath of the September 11 Terrorist Attacks,” November 2001. http://www.scu.edu/ethics/publications/briefings/privacy.html
 J. W. Seifert, “Data Mining and Homeland Security: An Overview,” CRS Report for Congress, (RL31798), January 2006. http://www.fas.org/sgp/crs/intel/RL31798.pdf
 T. Fawcett and F. Provost, “Activity Monitoring: Noticing Interesting Changes in Behavior,” Proceedings of the 5th ACM International Conference on Knowledge Discovery and Data Mining (SIGKDD), San Diego, 1999, pp. 53-62. doi:10.1145/312129.312195
 C. Clifton, M. Kantarcioglu, J. Vaidya, X. Lin and M. Y. Zhu, “Tools for Privacy-Preserving Distributed Data Mining,” ACM SIGKDD Explorations Newsletter, Vol. 4, No. 2, 2002, pp. 28-34. doi:10.1145/ 772862.772867
 S. Agrawal and J. R. Haritsa, “A Framework for HighAccuracy Privacy-Preserving Mining,” Proceedings of the 21st IEEE International Conference on Data Engineering (ICDE), Tokyo, April 2005, pp. 193-204. doi:10.1109/ICDE.2005.8
 K. Liu, H. Kargupta and J. Ryan, “Random ProjectionBased Multiplicative Perturbation for Privacy-Preserving Distributed Data Mining,” IEEE Transactions on Knowledge and Data Engineering (TKDE), Vol. 18, No. 1, 2006, pp. 92-106. doi:10.1109/TKDE.2006.14
 V. S. Verykios, E. Bertino, I. N. Fovino, L. P. Provenza, Y. Saygin and Y. Theodoridis, “State-of-the-Art in Privacy Preserving Data Mining,” ACM SIGMOD Record, Vol. 3, No. 1, 2004, pp. 50-57. doi:10.1145/974121.974131
 W. Du, Y. S. Han and S. Chen, “Privacy-Preserving Multivariate Statistical Analysis: Linear Regression and Classification,” Proceedings of the SIAM International Conference on Data Mining (SDM), Florida, 2004.
 W. Du and Z. Zhan, “Building Decision Tree Classifier on Private Data,” Workshop on Privacy, Security, and Data Mining at the 2002 IEEE International Conference on Data Mining, Maebashi City, December 2002.
 A. W. C. Fu, R. C. W. Wong and K. Wang, “Privacy-Preserving Frequent Pattern Mining across Private Databases,” Proceedings of the 5th IEEE International Conference on Data Mining (ICDM), Houston, November 2005, pp. 613-616.
 M. Kantarcioglu and C. Clifton, “Privacy-Preserving Data Mining of Association Rules on Horizontally Partitioned Data,” IEEE Transactions on Knowledge and Data Engineering (TKDE), Vol. 16, No. 9, 2004, pp. 1026-1037. doi:10.1109/TKDE.2004.45
 M. Kantarcioglu and C. Clifton, “Privately Computing a Distributed K-Nn Classifier,” Proceedings of the 8th European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD), Pisa, September 2004, pp. 279-290.
 J. Vaidya and C. Clifton, “Privacy-Preserving Association Rule Mining in Vertically Partitioned Data,” Proceedings of the 8th ACM International Conference on Knowledge Discovery and Data Mining (SIGKDD), Edmonton, 2002. pp. 639-644.
 J. Vaidya and C. Clifton, “Privacy-Preserving k-Means Clustering over Vertically Partitioned Data,” Proceedings of the 9th ACM International Conference on Knowledge Discovery and Data Mining (SIGKDD), Washington, 2003, pp. 206-215.
 Z. Yang, S. Zhong and R. N. Wright, “Privacy-Preserving Classification of Customer Data without Loss of Accuracy,” Proceedings of the 5th SIAM International Conference on Data Mining (SDM), Newport Beach, 2005, pp. 92-102.
 A. Blum, C. Dwork, F. McSherry and K. Nissim, “Practical Privacy: The Sulq Framework,” Proceedings of the 24th ACM Symposium on Principles of Database Systems (PODS), Baltimore, June 2005, pp. 128-138.
 A. Blum, K. Ligett and A. Roth, “A Learning Theory Approach to Non-Interactive Database Privacy,” Proceedings of the 40th annual ACM Symposium on Theory of Computing (STOC), Victoria, 2008, pp. 609-618.