Privacy Preserving Data Mining: Techniques and Algorithms
|International Journal of Engineering Trends and Technology (IJETT)||
|© 2020 by IJETT Journal|
|Year of Publication : 2020|
|Authors : Ritu Ratra, Preeti Gulia
|DOI : 10.14445/22315381/IJETT-V68I11P207|
MLA Style: Ritu Ratra, Preeti Gulia "Privacy Preserving Data Mining: Techniques and Algorithms" International Journal of Engineering Trends and Technology 68.11(2020):56-62.
APA Style:Ritu Ratra, Preeti Gulia. Privacy Preserving Data Mining: Techniques and Algorithms International Journal of Engineering Trends and Technology, 68(11),56-62.
There is incredible volume of data that is generated at exponential rate by various organizations such as hospitals, insurance companies, banks, stock market etc. It is done by excellence of digitization of technology. It is well known that very large amount of data is being generated by different electronic devices. This data could be processed to help decision making. However data analytics is prone to privacy violations. There is no doubt that the data analytics is extremely helpful in decision making process, but it will cause some serious privacy concerns. So protect the individual privacy in the process of data analytics became most important and necessary task. In this paper, various threats related to privacy are examined. Techniques and models of privacy preserving are also discussed limitations. Nowadays the role of algorithms of PPDM is very crucial. Today, no doubt a number of PPDM techniques have been grown to preserve the privacy of individual. Some of them are cryptography, secured sum algorithms, perturbation and k-anonymity. Here main focus is on current researches related to PPDM. The paper will enable to understand the different challenges that are confronted in PPDM. It will also help to learn and apply the best applicable technique according to different data circumstances..
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Anonymization, cryptography, Neural Network, Perturbation, Privacy Preserving Data Mining Technique