Data Mining and Internet Profiling: Emerging Regulatory and Technical Approaches

Privacy and Security

Article Snapshot


Ronald D. Lee, Ira Rubinstein and Paul M. Schwartz


University of Chicago Law Review, Vol. 75, No. 1, pg. 261, 2008


This article analyzes issues arising out of methods to protect privacy within the context of government data mining.

Policy Relevance

Protecting privacy within the context of data mining. Addressing the potential negative impact privacy protections might have on the use of data mining for counter-terrorism purposes.

Main Points

  • The general consensus among the policy community is that data mining has substantial potential to protect against terrorism but technological and legal safeguards are needed.
  • Elements of a generally accepted framework for government data mining that will protect privacy include:
    • Data mining only when specific legal authorization is granted.
    • Technologies to restrict unauthorized use of data mining tools.
    • Rule-based processing to tailor data search queries to the analyst's authorization.
    • Minimization of the amount of personal information revealed.
    • Audits system to prevent misuse of data mine access.
    • Addressing appropriate means to deal with false positives.
    • Accountability measures that provide independent validation of the data mining's predictive accuracy.
  • Digital identity management systems, which anonymize users online, are another way to protect users privacy with regards to data mining.
  • The use of a privacy-protecting framework for government data mining may actually make data mining more accurate.
  • The use of privacy-protecting online identity management systems and online anonymity and pseudonymity in general may hamper the government's ability to identify online users and thus undermine terrorism prevention efforts. This issue will need to be addressed if adoption of such technology increases.
  • Transparency is needed in both data-mining and online identity management systems in order to have an informed debate about how to best regulate data mining.

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