Privacy Preserving Data Mining Technique - Issues and Challenges
DOI:
https://doi.org/10.18486/ijcsnt/8.2.104Keywords:
Privacy Preserving Data Mining, Multi Party Data Source, Objective Summarization, Model Improvements, Techniques and ToolsAbstract
The technology improves the ability of data storage and their processing. Therefore a significant growth on different business domains is observed. The business intelligence requires data and consumer habits but the requirement of data is never fulfilled by a single data source. In this context required to combine the different source of information for making smart decisions or for finding the conclusions. But the limitation is that nobody wants to disclose their client's private and sensitive information to others. Therefore we need a privacy preserving environment to handle the data, it's privacy and conclusion. In this presented paper privacy preserving data mining is the key area of investigation using the available literature. Additionally by concluding the literature need to establish the future prospects for the work.
References
Mukhopadhyay A, Maulik U, Bandyopadhyay S et al. A survey of multi-objective evolutionary algorithms for data mining: Part I. IEEE Transactions on Evolutionary Computation 2014; 18(1): 20–35. DOI: https://doi.org/10.1109/TEVC.2013.2290082
Aldeen YAAS, Salleh M and Razzaque MA. A comprehensive review on privacy preserving data mining. SpringerPlus 2015; 4(694): 1–36. DOI: https://doi.org/10.1186/s40064-015-1481-x
Danasana J, Kumar R and Dey D. Mining association rules for horizontally partitioned databases using CK secure sum technique. International Journal of Distributed and Parallel Systems 2012; 3(6): 149–157. DOI: https://doi.org/10.5121/ijdps.2012.3613
Ponce J and Karahoca A. Data Mining and Knowledge Discovery in Real Life Applications. Croatia: InTech, 2009. DOI: https://doi.org/10.5772/97
Mendes R and Vilela JP. Privacy-preserving data mining: Methods, metrics, and applications. IEEE Access 2016; 5: 10562–10582. DOI: https://doi.org/10.1109/ACCESS.2017.2706947
Xu L, Jiang C, Wang J et al. Information security in big data: Privacy and data mining. IEEE Access 2014; 2: 1149–1176. DOI: https://doi.org/10.1109/ACCESS.2014.2362522
Vasan KK and Surendiran B. Dimensionality reduction using principal component analysis for network intrusion detection. Perspectives in Science 2016; 8: 510–512. DOI: https://doi.org/10.1016/j.pisc.2016.05.010
Nettleton DF, Orriols-Puig A and Fornells A. A study of the effect of different types of noise on the precision of supervised learning techniques. Artificial Intelligence Review 2010; 33(4): 275–306. DOI: https://doi.org/10.1007/s10462-010-9156-z
Anitha P, Krithka G and Choudhry MD. Machine learning techniques for learning features of any kind of data: A case study. International Journal of Advanced Research in Computer Engineering & Technology 2014; 3(12): 4324–4331.
Chitradevi B and Thinaharan N. Role of decision making in data mining systems. International Journal of Trend in Research and Development 2015; 2(5): 122–125.
Swamy SK, Manjula SH, Venugopal KR et al. Association rule sharing model for privacy preservation and collaborative data mining efficiency. In RAECS 2014 Proceedings of 2014 Recent Advances in Engineering and Computer Sciences. Chandigarh, India: IEEE, pp. 1–6. DOI: https://doi.org/10.1109/RAECS.2014.6799597
Basiri A, Amirian P, Winstanley A et al. Making tourist guidance systems more intelligent, adaptive and personalised using crowd sourced movement data. Journal of Ambient Intelligence and Humanized Computing 2017; 9: 413. https://doi.org/10.1007/s12652-017-0550-0. DOI: https://doi.org/10.1007/s12652-017-0550-0
Kou G, Peng Y, Shi Y et al. Privacy-preserving data mining of medical data using data separation-based techniques. Data Science Journal 2007; 6: 429–434. DOI: https://doi.org/10.2481/dsj.6.S429
Chen CLP and Zhang CY. Data-intensive applications, challenges, techniques and technologies: A survey on big data. Information Sciences 2014; 275: 314–347. https://doi.org/10.1016/j.ins.2014.01.015. DOI: https://doi.org/10.1016/j.ins.2014.01.015
Xu C, Tao D, Xu C et al. Large-margin weakly supervised dimensionality reduction. In Proceedings of the 31st International Conference on Machine Learning. Beijing, China, pp. 865–873.
Bouzas D, Arvanitopoulos N and Tefas A. Graph embedded nonparametric mutual information for supervised dimensionality reduction. IEEE Transactions on Neural Networks and Learning Systems 2015; 26(5): 957–963. DOI: https://doi.org/10.1109/TNNLS.2014.2329240
Sunitha L, Raju MB and Srinivasa BS. A comparative study between noisy data and outlier data in data mining. International Journal of Current Engineering and Technology 2013; 3(2): 575–577.
Xiong H, Pandey G, Steinbach M et al. Enhancing data analysis with noise removal. IEEE Transactions on Knowledge and Data Engineering 2006; 18(3): 304–319. DOI: https://doi.org/10.1109/TKDE.2006.46
Zhang W, Lin Y, Xiao S et al. Privacy preserving ranked multi-keyword search for multiple data owners in cloud computing. IEEE Transactions on Computers 2015; 6(1): 1–14. DOI: https://doi.org/10.1109/INDICON.2016.7838916
Kung SY, Chanyaswad T, Chang JM et al. Collaborative PCA/DCA learning methods for compressive privacy. ACM Transactions on Embedded Computing Systems 2017; 16(3): 77–117. DOI: https://doi.org/10.1145/2996460
Zhang Q, Yang LT and Chen Z. Privacy preserving deep computation model on cloud for big data feature learning. IEEE Transactions on Computers 2016; 65(5): 1351–1362. DOI: https://doi.org/10.1109/TC.2015.2470255
Nissim M, Rokach L and Maimon O. Privacy-preserving data mining: A feature set partitioning approach. Information Sciences 2010; 180(14): 2696–2720. DOI: https://doi.org/10.1016/j.ins.2010.03.011
Poovammal E and Ponnavaikko M. Utility independent privacy preserving data mining on vertically partitioned data. Journal of Computer Science 2009; 5(9): 666–673. DOI: https://doi.org/10.3844/jcssp.2009.666.673
Li T, Li N, Zhang J et al. Slicing: A new approach for privacy preserving data publishing. IEEE Transactions on Knowledge and Data Engineering 2012; 24(3): 561–574. DOI: https://doi.org/10.1109/TKDE.2010.236
Fletcher S and Islam MZ. Measuring information quality for privacy preserving data mining. International Journal of Computer Theory and Engineering 2015; 7(1): 21–28. DOI: https://doi.org/10.7763/IJCTE.2015.V7.924
Hua J, Tang A, Fang Y et al. Privacy-preserving utility verification of the data published by non-interactive differentially private mechanisms. IEEE Transactions on Information Forensics and Security 2016; 11(10): 2298–2311. DOI: https://doi.org/10.1109/TIFS.2016.2532839
Li L, Lu R, Choo KKR et al. Privacy-preserving outsourced association rule mining on vertically partitioned databases. IEEE Transactions on Information Forensics and Security 2016: 1847–1861. DOI: https://doi.org/10.1109/TIFS.2016.2561241
Li Y, Chen M, Li Q et al. Enabling multi-level trust in privacy preserving data mining. IEEE Transactions on Knowledge and Data Engineering 2012; 24(9): 1598–1612. DOI: https://doi.org/10.1109/TKDE.2011.124
Arribas N, Torra GV, Erola A et al. User k-anonymity for privacy preserving data mining of query logs. Information Processing & Management 2012; 48(3): 476–487. DOI: https://doi.org/10.1016/j.ipm.2011.01.004
Downloads
Published
Issue
Section
License
Copyright (c) 2019 Mayur Rathi, Anand Rajavat

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.