PRIVACY ISSUES IN KNOWLEDGE DISCOVERY AND DATA MINING
Ljiljana Branković, Vladimir Estivill‐Castro · 2000
Recent developments in information technology have enabled collection and processing of vast amounts of personal data, such as criminal records, shopping habits, credit and medical history, and driving records. This information is undoubtedly very useful in many areas, including medical research, law enforcement and national security. However, there is an increasing public concern about the individuals' privacy. Privacy is commonly seen as the right of individuals to control information about themselves. The appearance of technology of knowledge Discovery and Data Mining (KDDM) has revitalized concern about the following general privacy issues: secondary use of the personal information, handling misinformation, and granulated access to personal information. These issues demonstrate the existing privacy laws and policies are well behind the developments in technology, and no longer offer adequate protection. We discuss new privacy threats posed by KDDM. KDDM technology includes massive data collection, data warehouses, statistical analysis and deductive learning techniques, and uses vast amounts of data to generate hypotheses and discover general patterns. KDDM poses the following new challenges to privacy: stereotypes, guarding personal data from KDDM researches, protecting privacy of individuals from training sets, and combination of patterns. We discuss the possible solutions and their impact on the quality of discovered patterns.