Maheyzah Md Siraj
ABSTRACT
In recent years, the importance of the Internet in our personal as well as our professional lives cannot be overstated as can be observed from the immense increase of its users. It therefore comes as no surprise that a lot of businesses are being carried out over the internet. It brings along privacy threats to the data and information of an organization. Data mining is the processing of analyze larger data in order to discover patterns and analyze hidden data concurring to distinctive sights for categorize into convenient information which is collected and assembled in common areas and other information necessities to eventually cut costs and increase revenue. In fact, the data mining has emerged as a significant technology for gaining knowledge from vast quantities of data. However, there was been growing concern that use of this technology is violating individual privacy. This tool aims to find useful patterns from large amount of data using by mining algorithms and approaches. The analysis of privacy preserving data mining (PPDM) algorithms should consider the effects of these algorithms in mining the results as well as in preserving privacy. Therefore, the success of privacy preserving data mining algorithms is measured in term of its performances, data utility, level of uncertainty, data anonymization, data randomization and so on based on data mining techniques and approaches are presented in this paper to analyze.
- Shrada Patel, Ronak Patel, "A Review on Privacy Preserving Data Mining", IJSDR (International Journal for Scientific Research & Developmental) vol 3: 2321--0613, 2016.Google Scholar
- M. B. Malik, M. A. Ghazi and R. Ali, "Privacy Preserving Data Mining Techniques: Current Scenario and Future Prospects", in proceedings of Third International Conference on Computer and Communication Technology, IEEE 2012. Google ScholarDigital Library
- Bhargav Sundararajan, Deeprthi Peri, Nita Radhakrishnan, Mehul Awasthi, "An Extensive Survey of Privacy Preserving Data Mining Techniques", IJCSN International Journal of Computer Science and Network, V olume 6, Issues 5, October 2017.Google Scholar
- Vinoth Kumar J, Santhi V, "A Brief Survey on Privacy Techniques in Data Mining", IOSRH Journal of Computer Engineering (IOSR-JCE), volume 18, Issue 4, July-August 2016.Google Scholar
- Matwin, S., "Privacy Preserving Data Mining Techniques: Survey and Challenges", in Discrimination and Privacy in the Information Society: 209--221. Springer Berlin Heidelberg, 2013.Google Scholar
- Alaa H Hamami, Suhad Abu Shehab, "An Approach for Privacy Preserving and Knowledge In Data Mining Application", Journal of Emerging Trends in Computing and Information Sciences, vol 4: ISSN 2079-8407, January 2013.Google Scholar
- R. Agarawal and R. Srikant. "Privacy Preserving Data Mining", ACM SIGMOD Conference on Management of Data, pp:439--450, 2000. Google ScholarDigital Library
- Ella Bingham and Heikki Mannila, "Random Projection in Dimensionality Reduction: Application to Image and Text Data", ACM KDD San Francisco CA USA, 2011. Google ScholarDigital Library
- Tom Krenzke, Katie Hubbell, Mamadou Diallo, Amita Gopinath and Sixia Chen, "Data Coarsening and Data Swapping Algorithms", 2014.Google Scholar
- Mynavathi R., Sowmiya N. And V anitha D., "Survey of Various Techniques to Provide Multilevel Trust in Privacy Preserving Data Mining", International Journal of Engineering Science and Technology, volume 3(3): 2127--2133, 2014.Google Scholar
- Malik, M. B., Ghazi, M. A., Ali R, "Privacy Preserving Data Mining Techniques: Current Scenario and Future Prospects", Third International Conference on Computer and Communication Technology, IEEE:26--31, 2012. Google ScholarDigital Library
- Bhanumathi, S. And Sakthivel, "A New Model for Privacy Preserving Multiparty Collaborative Data Mining".Google Scholar
- International Conferences on Circuits Power and Computing Technologies (ICCPCT-2013), IEEE:845--850, 2013.Google Scholar
- Ryan Stephens and Ron Plew, "The Database Normalization Process", Sans Teach Yourself SQL in 24 Hours, 3rd Edition, 2002.Google Scholar
- Malik, M. B., Ghazi, M. A., Ali R, "Privacy Preserving Data Mining Techniques: Current Scenario and Future Prospects", Third International Conference on Computer and Communication Technology, IEEE:26--31, 2012. Google ScholarDigital Library
- Nayan G. And Devi, "A Survey on Privacy Preserving Data Mining: Approaches and Techniques", International Journal of Engineering Science and Technology, volume 3(3):2127--2133, 2011.Google Scholar
- Mynavathi R., Sowmiya N. and Vanitha D., "Survey of Various Techniques to Provide Multilevel Trust in Privacy Preserving Data Mining", International Journnal of Engineering Science and Technology, volume 3(3): 217--2133, 2014.Google Scholar
- Vinoth Kumar J, Santhi V, "A Brief Survey on Privacy Techniques in Data Mining", IOSRH Journal of Computer Engineering (IOSR-JCE), volume 18, Issue 4, July-August 2016.Google Scholar
- Reena and R.Kuma, "Effect of Randomization for Privacy Preservation on Classification Tasks", Processing of the International Conference on Informatics and Analaytics(ICIA-16), Pondicherry India, 2016. Google ScholarDigital Library
Index Terms
- A Survey on Privacy Preserving Data Mining Approaches and Techniques
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