In the age of computer driven decision making the Data Science has become a vital important area for the parties storing the data. For the efficient use of available data; users need to excel in better Data Mining through robust Machine Learning techniques. Data mining applications are intensively used in government and corporate sector to analyze data for prediction, pattern recognitions, and classification. Accuracy of data mining algorithm depends on volume of training data. Advancement in computer and communication technologies allowed distributed computing environment, where multiple clients/parties can conduct joint learning process by incorporating distributed data. Distributed data may be arbitrary partitioned among parties. Recent data mining application uses power of cloud computing to execute complex computation involved in learning process. Despite of these advancements, individual or organizations holding data are reluctant to share their sensitive data due to fear of privacy breach and losses. Privacy Preserving Data Mining (PPDM) is solution to protect personal information while sharing it in distributed environment. Privacy preservation is achieved by data randomization or encryption techniques. Robust security to personal information and more accuracy in mining applications, offer more popularity to privacy preservation encryption techniques than data randomization techniques. Homomorphic encryption is one of the popular encryption techniques, where users can perform operations on cipher text; the results are similar to operations on their respective plaintexts. In present survey paper some data mining techniques like- ANN, RDT, SVM and Deep Learning, based on distributed partitioned data such are reviewed in special context to privacy preservation.

Data mining, Privacy preservation, homomorphic encryption, ANN, RDT, SVM, Deep Learning

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Authors Profile
S. B. Javheri in pursed Bachelor of Engineering from North Maharashtra University, Jalgaon, Marashatra, India in 1998 and Master of Engineering from B. V. D. University, Pune, Maharashtra, India in year 2009. He is currently pursuing Ph.D. from S.G.G.S.I.E.& T., Nanded, Maharashtra, India and currently working as Associate Professor in Department of Computer Engineering , JSPM’s Rajarshi Shahu College of Engineering, Pune since 2004. He has published 12 research papers in reputed international journals/conferences. His main research work focuses on information security, machine learning, Neural network. He has 19 years of teaching experience.

U. V. Kulkarni has obtained Bachelor of Engin- eering degree in Electronics from Marathwada University, Aurangabad, Maharashtra, India in 1987. He completed Master of Engineering in system software from Dr. Babasaheb Ambedkar Marathwada University, Aurangabad, Maharashtra, India in 1992. He has completed his Ph.D. in Electronics and Computer Science Engineering in 2002 from Swami Ramanand Teerth Marathwada University Nanded, Maharashtra, India. He is currently working as Professor and Head in Computer Science and Engineering Department at SGGSIE&T (Autonomous), Nanded, Maharashtra, India. He has received National Level Gold Medal and Computer Engineering Division Prize for the paper published in the Journal of Institution of Engineers, titled as Fuzzy Hypersphere Neural Network Classifier, May 2004 and the best paper award for the research paper presented in international conference held at Imperial College London, U.K., 2014. He has published many research papers in reputed National and International Journals. His areas of interest include Microprocessors, Data Structures, Distributed Systems, Fuzzy Neural Networks, and Pattern Classification. He has more than 30 years of teaching experience.