Surveillance privacy from Cameras
Researchers from MIT CSAIL Introduce ‘Privid’: an AI Tool, Build on Differential Privacy, to Guarantee Privacy in Video Footage from Surveillance Cameras
By Annu Kumari -March 30, 2022 This research summary article is based on the paper 'Privid: Practical, Privacy-Preserving Video Analytics Queries' and MIT article 'Security tool guarantees privacy in surveillance footage'
Surveillance cameras have an identity crisis exacerbated by a conflict between function and privacy. Machine learning techniques have automated video content analysis on a vast scale as these sophisticated small sensors have shown up seemingly everywhere. Still, with increased mass monitoring, there are currently no legally enforceable standards to curb privacy invasions.
Security cameras have evolved into wiser and more capable tools than the grainy images of the past, which were frequently used as the “hero tool” in crime dramas. Video surveillance can now assist health regulators in determining the percentage of persons using masks, transportation departments in monitoring the density and flow of automobiles, cyclists and walkers, and businesses in gaining a better understanding of buying habits. But why has privacy remained a second-class citizen?
Privid
Currently, the footage is retrofitted with blurred faces or black boxes. This prevents analysts from asking some legitimate questions (for example, are people wearing masks? ). Dissatisfied with the present status quo, MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) developed a system with other institutions to better guarantee privacy in surveillance video footage. The system, dubbed “Privid,” allows analysts to input video data searches and then adds a tiny amount of noise (additional data) to the result to ensure that no one can be identified. The method is based on a formal notion of privacy known as “differential privacy,” which permits without having access to aggregate statistics about private data disclosing individually identifying information. ... '
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