One key method for ensuring privacy while processing large amounts of data is de-identification. De-identified data refers to data through which a link to a particular individual cannot be established. This often involves “scrubbing” the identifiable elements of personal data, making it “safe” in privacy terms while attempting to retain its commercial and scientific value.
In the era of big data, the debate over the definition of personal information, de-identification and re-identification has never been more important. Privacy regimes often rely on data being considered Personal in order to require the application of privacy rights and protections. Data that is anonymous is considered free of privacy risk and available for public use.
Yet much data that is collected and used exists somewhere on a spectrum between these stages. FPF’s De-ID Project has examined practical frameworks for applying privacy restrictions to data based on the nature of data that is collected, the risks of de-identification, and the additional legal and administrative protections that may be applied.
