@article{MAKHILLJEAS201914117309,
    title = {A Combined Approach for Privacy Preserving Classification Mining},
    journal = {Journal of Engineering and Applied Sciences},
    volume = {14},
    number = {1},
    pages = {188-194},
    year = {2019},
    issn = {1816-949x},
    doi = {jeasci.2019.188.194},
    url = {https://makhillpublications.co/view-article.php?issn=1816-949x&doi=jeasci.2019.188.194},
    author = {Naga Prasanthi and},
    keywords = {data perturbation,k-anonymization,purposes,secure
multiparty,privacy preserving data mining,Data mining},
    abstract = {In recent years, the growing capacity of information storage devices has led to increased storing
personal information about customers and individuals for various purposes. Data mining needs extensive
amount of data to do analysis for finding out patterns and other information which could be helpful for
business growth, tracking health data, improving services, etc. This information can be misused for many
reasons like identity theft, fake credit/debit card transactions, etc. To avoid these situations, data mining
techniques which secure privacy are proposed. Data perturbation, knowledge hiding, secure multiparty
computation and privacy aware knowledge sharing are some of the techniques of privacy preserving data
mining. A combination of these approaches is applied to get better privacy. In this study, we discuss in detail
about geometric data perturbation technique and k-anonymization technique and prove that data mining results
after perturbation and anonymization also are not changed much.}
    }