Often organizations release and receive medical data with all explicit identifiers, such as name, address, phone number, and Social Security number, removed in the incorrect belief that patient confidentiality is maintained because the resulting data look anonymous; however, we show that in most...

Today most organizations release and receive medical data with all explicit identifiers, such as name, address, and phone number, removed in the incorrect belief that patient confidentiality is maintained because the resulting data look anonymous. We examine three computer programs that do...

Sharing medical data with researchers, economists, policy makers, administrators and other secondary viewers, immediately summons for consideration the dichotomy between the recipient’s needs and disclosure risk. Finding the optimal balance between the suppression of details within the...

Today's globally networked society places great demand on the dissemination and sharing of person-specific data. Situations where aggregate statistical information was once the reporting norm now rely heavily on the transfer of microscopically detailed transaction and encounter information. This...

We present a computer program named Datafly that uses computational disclosure techniques to maintain anonymity in medical data by automatically generalizing, substituting and removing information as appropriate without losing many of the details found within the data. Decisions are made at the...