| Towards Privacy-Preserving Data Mining in Law Enforcement (2009) | |||||||||
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| For law enforcement to be effective, it needs to extract previously unknown knowledge from largeamounts of different types of data. Data mining is the most compelling tool for this task as it is motivated bysuccessful applications in numerous domains. Therefore, many believe that data mining can significantly improvethe execution of law enforcement. However, a severe problem occurs when data mining is applied: manyinevitable mistakes result in privacy violations. Recently, we developed a new approach to data mining, called theROC isometrics approach, which is proven to produce reliable outputs in the sense that we can set the number ofmistakes before the data mining is actually applied. In the paper, we determine the implications of the approach tolaw enforcement and we propose several recommendations for legislations that try to deal with data mining. As aresult, we may conclude that the ROC isometrics approach allows for privacy-preserving data mining so that lawenforcement becomes more effectively and efficiently than so far. | |||||||||
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