Nowadays, Data Mining is a popular tool for extracting hidden knowledge from huge amount of data. To find hidden knowledge in the data without revealing sensitive information is one of the major challenges in data mining. There are many strategies have been proposed to hide the sensitive information. Association rule mining is one of the data mining techniques used to extract hidden knowledge from large datasets. This hidden knowledge contains most of the times confidential information that the users want to keep private or do not want to disclose to public. Therefore, privacy preserving data mining (PPDM) techniques are used to preserve such confidential information or restrictive pattern from unauthorized access. In this paper, all the approaches for hiding sensitive association rules in PPDM have been compared theoretically and points out their pros and cons.

Data Mining, Association rule mining, privacy preserving data mining (PPDM)

[1] Shubhra Rana, Dr. P. anthi Thilagam, “Hierarchical Homomorphic Encryption based Privacy Preserving Distributed Association Rule Mining”. IEEE 13th International Conference on Information Technology, 2014.
[2] Rachit V. Adhvaryu, Nikunj H. Domadiya, “Privacy Preserving in Association Rule Mining On Horizontally Partitioned Database”. International Journal of Advanced Research in Computer Engineering & Technology (IJARCET), Vol. 3, Issue 5, May 2014.
[3] Vaishali Patil, Ramesh Vasappanavara, Tushar Ghorpade, “Securing association rule mining with FP growth algorithm in horizontally partitioned database”. International Conference on Control, Computing, Communication and Materials (ICCCCM), 2016.
[4] Lichun Li, Rongxing Lu, Kim-Kwang Raymond Choo, Anwitaman Datta, and Jun Shao, “Privacy-Preserving Outsourced Association Rule Mining on Vertically Partitioned Databases”. IEEE, 2016.
[5] Yaoan Jin, Chunhua Su, Na Ruan, and Weijia Jia, “Privacy-Preserving Mining of Association Rules for Horizontally Distributed Databases Based on FP-Tree”. Springer International Publishing AG, 2016.
[6] Golnar Assadat Afzali, Shahriar Mohammadi Jia, “Privacy preserving big data mining: association rule hiding using fuzzy logic approach”. The Institution of Engineering and Technology, 2017.
[7] Umesh Kumar Sahu, Anju Singh, “Approaches for Privacy Preserving Data Mining by Various Associations Rule Hiding Algorithms – A Survey”. International Journal of Computer Applications, 2016.
[8] Shabnum Rehman and Anil Sharma, “Privacy Preserving Data Mining Using Association Rule Based on Apriori Algorithm”. Springer Nature Singapore Pte Ltd, 2017.
[9] Narges Jamshidian Ghalehsefidi,, Mohammad Naderi Dehkord, “A Hybrid Algorithm based on Heuristic Method to Preserve Privacy in Association Rule Mining”. Indian Journal of Science and Technology,2016.
[10] D. Menaga, S. Revathi, “Least lion optimisation algorithm (LLOA) based secret key generation for privacy preserving association rule hiding”. The Institution of Engineering and Technology, 2018.
[11] Chun-WeiLin, Tzung-PeiHong, Hung-ChuanHsu, “Reducing Side Effects of Hiding Sensitive Itemsets in Privacy Preserving Data Mining”. Hindawi Publishing Corporation the Scientific World Journal, 2014.
[12] Bettahally N. Keshavamurthy, Asad M. Khan, Durga Toshniwal, “Privacy preserving association rule mining over distributed databases using genetic algorithm”. Neural Comput & Applic, 2013.
[13] Baocang Wang, Yu Zhan, and Zhili Zhang, “Cryptanalysis of a Symmetric Fully Homomorphic Encryption Scheme”. IEEE, 2017.
[14] P. Amaranatha Reddy, MHM Krishna Prasad, “Challenges to find Association Rules over various types of data items: a Survey”. International Conference on Computing, Communication and Automation(ICCCA), 2017.
[15] Chan Man Kuok, Ada Fu, Man Hon Wong, “Mining Fuzzy Association Rules in Databases”.
[16] Harendra chahar, B N keshavamurthy, chirag modi, “Privacy-preserving distributed mining of association rules using Elliptic-curve cryptosystem and Shamir’s secret sharing scheme”. Indian Academy of Sciences, 2017.