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.
Twelve Privacy Investments for Your Company for a Stronger 2025
FPF has put together a list of Twelve Privacy Investments for Your Company for a Stronger 2025 that reflects on new perspectives on the work that privacy teams do at their organizations. We hope there is something here that’s useful where you work, and we’d love to hear other ideas and feedback. Privacy Investments for Your […]
FPF’s Year in Review 2024
With contributions from Judy Wang, Communications Intern 2024 was a landmark year for the Future of Privacy Forum, as we continued to grow our privacy leadership through research and analysis, domestic and global meetings, expert testimony, and more – all while commemorating our 15th anniversary. Expanding our AI Footprint While 2023 was the year of […]