A Study of Various Privacy Preserving Data Ming Algorithms for Datasets

Privacy, security and accuracy are the major issues to be concern in field of data mining data mining when data is shared. A number of data mining algorithms are already introduced for big data when we are talking about Privacy preserving data mining. These algorithms categories data into groups. Further these groups can be used for extract useful information. Such kind of data is used in surveys, calculations etc. An election data can be considered as an example for such kind of groups. The groups are made in a, b, ab, cd category. Each group is not aware that which group has which data. In the end using data mining algorithms the desired data can be extracted

A Survey on Privacy for Sensitive Big Data by DM Algorithms

Whenever big data term is concerned the most important concern is privacy of data. One of the most common methods use random permutation techniques to mask the data, for preserving the privacy of sensitive data. Randomize response (RR) techniques were developed for the purpose of protecting surveys privacy and avoiding biased answers. The proposed work is to enhance the privacy level in RR technique using four group schemes. First according to the algorithm random attributes a, b, c, d were considered, then the randomization have been performed on every dataset according to the values of theta. Then ID3 and CART algorithm are applied on the randomized data

A Study on privacy for Sensitive Data by DM algorithms

Whenever big data term is concerned the most important concern is privacy of data. One of the most common methods use random permutation techniques to mask the data, for preserving the privacy of sensitive data. Randomize response (RR) techniques were developed for the purpose of protecting surveys privacy and avoiding biased answers. The proposed work is to enhance the privacy level in RR technique using four group schemes. First according to the algorithm random attributes a, b, c, d were considered, then the randomization have been performed on every dataset according to the values of theta. Then ID3 and CART algorithm are applied on the randomized data

Randomized Response Technique in Data Mining

Data mining is a process in which data is collected from different sources and resume it in useful information. Data mining is also known as knowledge discovery in database (KDD).Privacy and accuracy are the important issues in data mining when data is shared. A fruitful direction for future data mining research will be the development of techniques that incorporate privacy concerns. Most of the methods use random permutation techniques to mask the data, for preserving the privacy of sensitive data. Randomize response techniques were developed for the purpose of protecting surveys privacy and avoiding answers bias mainly. In RR technique it adds certain degree of randomness to the answer to prevent the data. The objective of this thesis is to enhance the privacy level in RR technique using four group schemes. First according to the algorithm random attributes a, b, c, d were considered, Then the randomization have been performed on every dataset according to the values of theta. Then ID3 and CART algorithm was applied on the randomized data. The result shows that by increasing the group, the privacy level will increase

Randomized Response Technique in Data Mining

Data mining is a process in which data is collected from different sources and resume it in useful information. Data mining is also known as knowledge discovery in database (KDD). Privacy and accuracy are the important issues in data mining when data is shared. A fruitful direction for future data mining research will be the development of techniques that incorporate privacy concerns. Most of the methods use random permutation techniques to mask the data, for preserving the privacy of sensitive data. Randomize response techniques were developed for the purpose of protecting surveys privacy and avoiding answers bias mainly. In RR technique it adds certain degree of randomness to the answer to prevent the data. The objective of this thesis is t o enhance the privacy level in RR technique using four group schemes. First according to the algorithm random attributes a, b, c, d wer e considered, Then the randomization have been performed on every dataset according to the values of theta. Then ID3 and CART algorithm was applied on the randomized data. The result shows that by increasing the group, the privacy level will increase

Randomized Response Technique in Data Mining

Data mining is a process in which data is collected from different sources and resume it in useful information. Data mining is also known as knowledge discovery in database (KDD).Privacy and accuracy are the important issues in data mining when data is shared. A fruitful direction for future data mining research will be the development of techniques that incorporate privacy concerns. Most of the methods use random permutation techniques to mask the data, for preserving the privacy of sensitive data. Randomize response techniques were developed for the purpose of protecting surveys privacy and avoiding answers bias mainly. In RR technique it adds certain degree of randomness to the answer to prevent the data. The objective of this thesis is to enhance the privacy level in RR technique using four group schemes. First according to the algorithm random attributes a, b, c, d were considered, Then the randomization have been performed on every dataset according to the values of theta. Then ID3 and CART algorithm was applied on the randomized data. The result shows that by increasing the group, the privacy level will increase

Performance Evaluation of K-Anonymized Data

Data mining provides tools to convert a large amount of knowledge data which is user relevant. But this process could return individual2019;s sensitive information compromising their privacy rights. So, based on different approaches, many privacy protection mechanism incorporated data mining techniques were developed. A widely used micro data protection concept is k-anonymity, proposed to capture the protection of a micro data table regarding re-identification of respondents which the data refers to. In this paper, the effect of the anonymization due to k-anonymity on the data mining classifiers is investigated. NaEF;ve Bayes classifier is used for evaluating the anonymized and non-anonymized data

Preserving The Safety And Confidentiality Of Data Mining Information In Health Care: A literature review

Daily, massive volume of data are produced due to the internet of things' rapid development, which has now permeated the healthcare industry. Recent advances in data mining have spawned a new field of a study dubbed privacy-preserving data mining (PPDM). PPDM technique or approach enables the extraction of actionable insight from enormous volume of data while safeguarding the privacy of individual information and benefiting the entire society Medical research has taken a new course as a result of data mining with healthcare data to detect diseases earlier and improve patient care. Data integration necessitates the sharing of sensitive patient information. However, substantial privacy issues are raised in connection with the storage and transmission of potentially sensitive information. Disclosing sensitive information infringes on patients' privacy. This paper aims to conduct a review of related work on privacy-preserving mechanisms, data protection regulations, and mitigating tactics. The review concluded that no single strategy outperforms all others. Hence, future research should focus on adequate techniques for privacy solutions in the age of massive medical data and the standardization of evaluation standards

Privacy Preserving Data Mining

Data mining techniques provide benefits in many areas such as medicine, sports, marketing, signal processing as well as data and network security. However, although data mining techniques used in security subjects such as intrusion detection, biometric authentication, fraud and malware classification, “privacy” has become a serious problem, especially in data mining applications that involve the collection and sharing of personal data. For these reasons, the problem of protecting privacy in the context of data mining differs from traditional data privacy protection, as data mining can act as both a friend and foe. Chapter covers the previously developed privacy preserving data mining techniques in two parts: (i) techniques proposed for input data that will be subject to data mining and (ii) techniques suggested for processed data (output of the data mining algorithms). Also presents attacks against the privacy of data mining applications. The chapter conclude with a discussion of next-generation privacy-preserving data mining applications at both the individual and organizational levels