A group of academics has designed a new system known as “Privid” that enables video analytics in a privacy-preserving manner to combat concerns with invasive tracking.
“We’re at a stage right now where cameras are practically ubiquitous. If there’s a camera on every street corner, every place you go, and if someone could actually process all of those videos in aggregate, you can imagine that entity building a very precise timeline of when and where a person has gone,” Frank Cangialosi, the lead author of the study and a researcher at the MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL), said in a statement.
“People are already worried about location privacy with GPS — video data in aggregate could capture not only your location history, but also moods, behaviours, and more at each location,” Cangialosi added.
Privid is built on the foundation of differential privacy, a statistical technique that makes it possible to collect and share aggregate information about users, while safeguarding individual privacy.
This is achieved by adding random noise to the results to prevent re-identification attacks. The amount of noise added is a trade-off – adding more noise makes the data more anonymous, but it also makes the data less useful – and it’s determined by the privacy budget, which ensures that the results are still accurate and at the same time configured low enough to prevent data leakage.
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