Ethical issues of Privacy Preservation in Data Mining vis-a-vis Quality of data

Abstract--Customer Relationship Management (CRM) is a business activity through which the policies and

processes, based on information technology can acquire and manage customer knowledge. CRM system in the

enterprises can collect, track and analyze information of every customer, further more understand customer needs

and meet them. Various applications of CRM like Profile analysis, Segmentation, Risk analysis, Attrition, Lifetime

Value of the customer etc. , need to look into the personal data for data prediction and finding trends.

Research in data mining has been done on discovering knowledge about the underlying data. Data mining tools are

used to maintain the data in data warehouse and predict the hidden patterns and present them in form of a model.

Strategic decisions about the customers can be taken based on the outcomes of these models. This gives rise various

ethical issues like invasion of privacy, deception, harm to the customers data, loss of customers identity etc. Privacypreserving

data mining (PPDM) deals with the trade-off between the effectiveness of the mining process and privacy

of the subjects, aiming at minimizing the privacy exposure with minimal effect on mining results.Privacy

preservation in data mining is a framework which allows computation of data over an entire data set without

compromising the privacy of the data of the customer. Most of the methods for privacy computation use some

transformation on the data in order to perform the privacy preservation. Though the issues of privacy preservation is

of data might be taken care but quality of data is also of concern.

This paper aims to review the existing literature regarding previous techniques for Privacy-preserving data mining

(PPDM) and then the researcher proposes to find the effectiveness of the quality of data under the paradigm of

privacy preservation during data mining and ethical issues which may arise due to it . Finally analyze the

observations and discuss the conclusions and future work.

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