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The pace at which the world has seen an
explosion in data, with 90% of the world’s data generated in the past 2 years,
technologies like data mining have proved to be laudable. Being an
interdisciplinary field, data mining influences human life in ways such as
healthcare, Business, government sector, education etc. For the above mentioned
reasons, it has gained momentum over the past few years. Much has been
advertised about how data mining is transforming lives across the globe but
like every other technology, it too has been facing various challenges.
Knowing a technology that has its hands on to almost all the data it has access
to, our brain automatically draws an image of a super power capable of
compromising our privacy and security. This is indeed a matter of concern since
if Data mining can be used for the good of mankind; it can also be used perhaps
to the detriment of mankind! 
The purpose of this abstract is to discuss, or rather put forth my views on the
darker side of the technology. A controversy involving data mining managed to
steal the spotlight in 2012 when a retail chain targeted a teenage girl in New
York and inferred from her buying habits that she was pregnant even before her
own family found out! This example shows how data mining can give us incredible
insights.

In lay man terms, ”Data mining is all about finding new and useful information in a lot
of data. Sometimes it is also called knowledge discovery in databases.”  Some of the hurdles data mining is facing in
its long run towards its goal are as follows:

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1) PRIVACY ISSUES :

People these days are very active on the internet,
activities such as online shopping, bill payments, online transactions, social
media etc are at an all time boom but at the same time, people are also afraid
of the fact that their personal information might be used in an unethical way ,
potentially causing them trouble. The major goal of every business is to
maximize profit and for that matter, collect customer information and find
trends accordingly.

2) SECURITY ISSUES :

Companies store information of their employees relating to
their date of birth, employee ids, payroll etc. Past experiences show that
databases of some big companies have been hacked by the hackers, stealing the
personal information of employees, like financial transactions etc.

3) MISUSE OF
INFORMATION/INACCURATE INFORMATION :

Data obtained after mining, earlier supposed to be used  for marketing purposes, may be misused.
Moreover, no technology is perfect, making data mining prone to mistakes, which
can have hazardous consequences.

 

Seeing
the rapid growth of human species technologically, It is expected that we
devise solutions to every problem that arises. Similarly,  the controversies related to data mining have
also been closely examined and following approaches have been used to solve
them:

1) LIMITING THE ACCESS.

Data provider can provide data to the
collector in either active or passive way. Providing data actively ensures that
the data collector can ignore the collector’s request for the information that
he thinks is sensitive. In case of passive, the data collector can take
measures in order to limit what the collector accesses. Security tools such as
anti-tracking extensions, advertisement blockers, anti-virus, anti-malware and
encryption tools prove to be of great help in the procedure.

2) TRADING PRIVACY FOR
BENEFIT.

It means that the data provider has to
choose between privacy of customer information and the benefits one can get out
of it. For example, a shopping website, if agrees to keep the customer data
safe and not leak it to a third party, may be provided access to the shopping
trends of a customer, ensuring customer satisfaction.

3) PROVIDING FALSE DATA

No matter how hard the data provider tries
to keep the access to data private, many unwanted users may still access it. In
order to deal with this problem, the data collectors prefer providing false
information to untrustworthy users instead of limiting access. The data can be
falsified in ways such as by using ‘sockpuppets’, using fake identity, using
security tools to mask identity etc.

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