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.
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 […]
Knowledge is Power: The Future of Privacy Forum launches FPF Training Program
“An investment in knowledge always pays the best interest”–Ben Franklin Let’s make 2023 the year we invest in ourselves, our teams, and the knowledge needed to best navigate this dynamic world of privacy and data protection. I am fortunate to know many of you who will read this blog post, but for those who I […]
If You Can't Take the Heat Map: Benefits & Risks of Releasing Location Datasets
Strava’s location data controversy demonstrates the unique challenges of publicly releasing location datasets (open data), even when the data is aggregated.
MAC Addresses and De-Identification
Location analytics companies log the hashed MAC address of mobile devices in range of their sensors at airports, malls, retail locations, stadiums and other venues. They do so primarily in order to create statistical reports that provide useful aggregated information such as average wait times on line, store “hot spots,” and the percentage of devices […]