Privacy in statistical databases: An approach using cell suppression

Although the Internet is a vast source of information for individuals, it is also a major source of information about individuals. Data collection through surveys, registration pages, user forms have resulted in more personal information being available than before. The biggest challenge with statistical databases is to protect the privacy of an individual when aggregate data is released for research purposes. Cell Suppression is a commonly used technique to protect sensitive data in published statistics. This involves the suppression of additional non-sensitive data to restrict inferences about the sensitive data. In this thesis, we suggest an alternative approach to cell suppression which we call cell blurring. The main idea is to replace some of the published numerical data by intervals. This approach has the advantage of distributing the uncertainty introduced by the suppression more evenly across the entire data