Security Tool Based on Surveillance Footage

 Have been thinking for sometime, how do you balance security and privacy in a world of quickly advancing and automated surveillance?  

Security tool guarantees privacy in surveillance footage  in MIT News

“Privid” could help officials gather secure public health data or enable transportation departments to monitor the density and flow of pedestrians, without learning personal information about people.

Rachel Gordon | MIT CSAIL,  March 28, 2022

Surveillance cameras have an identity problem, fueled by an inherent tension between utility and privacy. As these powerful little devices have cropped up seemingly everywhere, the use of machine learning tools has automated video content analysis at a massive scale — but with increasing mass surveillance, there are currently no legally enforceable rules to limit privacy invasions. 

Security cameras can do a lot — they’ve become smarter and supremely more competent than their ghosts of grainy pictures past, the ofttimes “hero tool” in crime media. (“See that little blurry blue blob in the right hand corner of that densely populated corner — we got him!”) Now, video surveillance can help health officials measure the fraction of people wearing masks, enable transportation departments to monitor the density and flow of vehicles, bikes, and pedestrians, and provide businesses with a better understanding of shopping behaviors. But why has privacy remained a weak afterthought? 

The status quo is to retrofit video with blurred faces or black boxes. Not only does this prevent analysts from asking some genuine queries (e.g., Are people wearing masks?), it also doesn’t always work; the system may miss some faces and leave them unblurred for the world to see. Dissatisfied with this status quo, researchers from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL), in collaboration with other institutions, came up with a system to better guarantee privacy in video footage from surveillance cameras. Called “Privid,” the system lets analysts submit video data queries, and adds a little bit of noise (extra data) to the end result to ensure that an individual can’t be identified. The system builds on a formal definition of privacy — “differential privacy” — which allows access to aggregate statistics about private data without revealing personally identifiable information.

Typically, analysts would just have access to the entire video to do whatever they want with it, but Privid makes sure the video isn’t a free buffet. Honest analysts can get access to the information they need, but that access is restrictive enough that malicious analysts can't do too much with it. To enable this, rather than running the code over the entire video in one shot, Privid breaks the video into small pieces and runs processing code over each chunk. Instead of getting results back from each piece, the segments are aggregated, and that additional noise is added. (There’s also information on the error bound you're going to get on your result — maybe a 2 percent error margin, given the extra noisy data added). 

For example, the code might output the number of people observed in each video chunk, and the aggregation might be the “sum,” to count the total number of people wearing face coverings, or the “average” to estimate the density of crowds. 

Privid allows analysts to use their own deep neural networks that are commonplace for video analytics today. This gives analysts the flexibility to ask questions that the designers of Privid did not anticipate. Across a variety of videos and queries, Privid was accurate within 79 to 99 percent of a non-private system.

“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,” says MIT CSAIL PhD student ​​Frank Cangialosi, the lead author on a paper about Privid. “People are already worried about location privacy with GPS — video data in aggregate could capture not only your location history, but also moods, behaviors, and more at each location.” 

Privid introduces a new notion of “duration-based privacy,” which decouples the definition of privacy from its enforcement — with obfuscation, if your privacy goal is to protect all people, the enforcement mechanism needs to do some work to find the people to protect, which it may or may not do perfectly. With this mechanism, you don’t need to fully specify everything, and you're not hiding more information than you need to.   .... '   (more with links to supporting papers)