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    Privacy Preserving Data Mining Operations without Disrupting Data Quality

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    IJCSN-2012-1-6-17.pdf
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    Description:
    Privacy Preserving Data Mining ...
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    Author
    B.Swapna
    R.VijayaPrakash
    Affiliation
    Department of CSE, SR Engineering College Warangal, Andhra Pradesh, India
    Associate Professor in Department of CSE SR Engineering College Warangal, Andhra Pradesh, India
    Issue Date
    2012-12-01
    Keywords
    data mining
    PPDM
    sanitization
    algorithms
    
    Metadata
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    Publisher
    International Journal of Computer Science and Network (IJCSN)
    Journal
    International Journal of Computer Science and Network (IJCSN)
    Description
    Data mining operations have become prevalent as they can extract trends or patterns that help in taking good business decisions. Often they operate on large historical databases or data warehouses to obtain actionable knowledge or business intelligence that helps in taking well informed decisions. In the data mining domain there came many tools to perform data mining operations. These tools are best used to obtain actionable knowledge from data. Manually doing this is not possible as the data is very huge and takes lot of time. Thus the data mining domain is being improved in a rapid pace. While data mining operations are very useful in obtaining business intelligence, they also have some drawbacks that are they get sensitive information from the database. People may misuse the freedom given by obtaining sensitive information illegally. Preserving privacy of data is also important. Towards this end many Privacy Preserving Data Mining (PPDM) algorithms came into existence that sanitize data to prevent data mining algorithms from extracting sensitive information from the databases.
    URI
    http://hdl.handle.net/10150/271473
    Additional Links
    http://ijcsn.org/IJCSN-2012/1-6/IJCSN-2012-1-6-17.pdf
    Abstract
    Data mining operations help discover business intelligence from historical data. The extracted business intelligence or actionable knowledge helps in taking well informed decisions that leads to profit to the organization that makes use of it. While performing mining privacy of data has to be given utmost importance. To achieve this PPDM (Privacy Preserving Data Mining) came into existence by sanitizing database that prevents discovery of association rules. However, this leads to modification of data and thus disrupting the quality of data. This paper proposes a new technique and algorithms that can perform privacy preserving data mining operations while ensuring that the data quality is not lost. The empirical results revealed that the proposed technique is useful and can be used in real world applications.
    Type
    Technical Report
    Language
    en
    Series/Report no.
    IJCSN-2012-1-6-17
    6
    ISSN
    2277-5420
    Collections
    DLIST

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