Transforming data to satisfy privacy constraints

Data on individuals and entities are being collected widely. These data can contain information that explicitly identifies the individual (e.g., social security number). Data can also contain other kinds of personal information (e.g., date of birth, zip code, gender) that are potentially identifying when linked with other available data sets. Data are often shared for business or legal reasons. This paper addresses the important issue of preserving the anonymity of the individuals or entities during the data dissemination process. We explore preserving the anonymity by the use of generalizations and suppressions on the potentially identifying portions of the data. We extend earlier works in this area along various dimensions. First, satisfying privacy constraints is considered in conjunction with the usage for the data being disseminated. This allows us to optimize the process of preserving privacy for the specified usage. In particular, we investigate the privacy transformation in th...

• Type: Conference
• Year: 2002
• Where: KDD
• Authors: Vijay S. Iyengar
• Keywords: Available Data Sets, Data Dissemination Process, Data Mining, Data Mining Applications, KDD 2002