| Summary: | 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. === 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.
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