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CRITICAL ISSUES IN DATA MINING: PRIVACY, NATIONAL SECURITY AND
PERSONAL LIBERTY IMPLICATIONS OF DATA MINING
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PRIVACY ISSUES OF DATA MINING Information available freely on the internet being used illegitimately against the person.
The social aspect of data mining privacy considering that different groups have different standards on social
The legal an political aspects including controls on the web and servers, the consequences of violating privacy policies and transfer of information between countries.
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CIVIL LIBERTIES V.S NATIONAL SECURITY Civil liberty involves everything from pro0tecting the rights of individuals, be they human, civil or privacy rights. Gathering information about people, mining data about them, surveillance on personal conversations may result in violation of their rights to privacy.
The main concern is whether to gather information and prosecute when violation on privacy occurs or wait until the national disasters occurs to gather data.
Among the questions that arise include Is it worth sacrificing privacy to ensure some level of national security
assuming the information is not misused?
Should national security be placed first and then prosecute those who violate privacy if its not possible to safeguard this private information?
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PRIVACY ENHANCED DATA MINING AND NATIONAL SECURITY
Since national security is very important to most nations in view of the terrorism activity the world over, one of the way to ensure privacy of personal information is through sensitive data mining.
Here, data mining is allowed but under enhanced privacy protection measures
Sensitive data mining involves determining, in advance those patterns are private and which are public since patterns are what constitutes the inference problem.
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SOLVING THE INFERENCE PROBLEM Bhavani Thuraisingham (2002) proposes the following measures to deal with the inference problem in data mining:
• Build an inference controller that detect possible inference problems by a user and restricting the use of those patterns
• The other strategy would be to classify the information such that the user would not be confident in the results and will therefore find the inferences made useless.
• Lastly, the person holding the data can test the data to see if any inferences can be made using the data. However, this approach is limited because on cannot predict all the inferences that could be made from the data or the combinnation of tools that will be used in data mining to get these inferences.
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REFERENCES
Farkas, C., & Jajodia, S. (2002). The Inference Problem: A Survey. Center for Secure Information Systems ,
Fairfax, VA.
Thuraisingham, B. (2005). Data Mining, National Security, Privacy and Civil Liberties. Arlington, VA:
Mendeley Ltd.
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