Towards real-time privacy-preserving video surveillance
<p dir="ltr">Video surveillance on a massive scale can be a vital tool for law enforcement agencies. To mitigate the serious privacy concerns of wide-scale video surveillance, researchers have designed secure and privacy-preserving protocols that obliviously match live feeds against...
محفوظ في:
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| مؤلفون آخرون: | , |
| منشور في: |
2021
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| _version_ | 1864513549205766144 |
|---|---|
| author | Elmahdi Bentafat (16896405) |
| author2 | M. Mazhar Rathore (16896399) Spiridon Bakiras (16896408) |
| author2_role | author author |
| author_facet | Elmahdi Bentafat (16896405) M. Mazhar Rathore (16896399) Spiridon Bakiras (16896408) |
| author_role | author |
| dc.creator.none.fl_str_mv | Elmahdi Bentafat (16896405) M. Mazhar Rathore (16896399) Spiridon Bakiras (16896408) |
| dc.date.none.fl_str_mv | 2021-12-01T00:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1016/j.comcom.2021.09.009 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Towards_real-time_privacy-preserving_video_surveillance/24420310 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Information and computing sciences Computer vision and multimedia computation Cybersecurity and privacy Video surveillance systems Privacy Face recognition License plate recognition Homomorphic encryption |
| dc.title.none.fl_str_mv | Towards real-time privacy-preserving video surveillance |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">Video surveillance on a massive scale can be a vital tool for law enforcement agencies. To mitigate the serious privacy concerns of wide-scale video surveillance, researchers have designed secure and privacy-preserving protocols that obliviously match live feeds against a suspects’ database. However, existing approaches are very expensive in terms of computation and communication costs and, as a result, they do not scale well for ubiquitous deployment. To this end, we propose a general framework for privacy-preserving identification that operates by storing an encrypted version of the suspects’ database at the video cameras. We show that this approach (i) reduces the protocol to a single round of communication between the camera and the server and (ii) speeds up the computation times significantly through the use of input-independent precomputations. We apply our framework to two practical use-cases, namely, face and license plate number recognition. In addition to the identification result, our face recognition protocol discloses some trivial information to the database server; however, this information is not sufficient for the server to infer any meaningful characteristics about the underlying individuals. On the other hand, the license plate recognition protocol is provably secure and can also handle minor character recognition errors that often occur in such systems. We implemented working prototypes of both surveillance systems and our experimental results are very promising. In the case of face recognition, and for a database of 100 suspects, the online computation time at the camera and the server is 155 ms and 34 ms, respectively, while the online communication cost is only 12 KB. Similarly, for a database of 3000 entries, license plate recognition requires only 232 ms and 75 ms at the camera and the server, respectively, while the online communication cost is 375 KB.</p><h2>Other Information</h2><p dir="ltr">Published in: Computer Communications<br>License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1016/j.comcom.2021.09.009" target="_blank">https://dx.doi.org/10.1016/j.comcom.2021.09.009</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_748e092167f745e22f1a2a053e4b2154 |
| identifier_str_mv | 10.1016/j.comcom.2021.09.009 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/24420310 |
| publishDate | 2021 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Towards real-time privacy-preserving video surveillanceElmahdi Bentafat (16896405)M. Mazhar Rathore (16896399)Spiridon Bakiras (16896408)Information and computing sciencesComputer vision and multimedia computationCybersecurity and privacyVideo surveillance systemsPrivacyFace recognitionLicense plate recognitionHomomorphic encryption<p dir="ltr">Video surveillance on a massive scale can be a vital tool for law enforcement agencies. To mitigate the serious privacy concerns of wide-scale video surveillance, researchers have designed secure and privacy-preserving protocols that obliviously match live feeds against a suspects’ database. However, existing approaches are very expensive in terms of computation and communication costs and, as a result, they do not scale well for ubiquitous deployment. To this end, we propose a general framework for privacy-preserving identification that operates by storing an encrypted version of the suspects’ database at the video cameras. We show that this approach (i) reduces the protocol to a single round of communication between the camera and the server and (ii) speeds up the computation times significantly through the use of input-independent precomputations. We apply our framework to two practical use-cases, namely, face and license plate number recognition. In addition to the identification result, our face recognition protocol discloses some trivial information to the database server; however, this information is not sufficient for the server to infer any meaningful characteristics about the underlying individuals. On the other hand, the license plate recognition protocol is provably secure and can also handle minor character recognition errors that often occur in such systems. We implemented working prototypes of both surveillance systems and our experimental results are very promising. In the case of face recognition, and for a database of 100 suspects, the online computation time at the camera and the server is 155 ms and 34 ms, respectively, while the online communication cost is only 12 KB. Similarly, for a database of 3000 entries, license plate recognition requires only 232 ms and 75 ms at the camera and the server, respectively, while the online communication cost is 375 KB.</p><h2>Other Information</h2><p dir="ltr">Published in: Computer Communications<br>License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1016/j.comcom.2021.09.009" target="_blank">https://dx.doi.org/10.1016/j.comcom.2021.09.009</a></p>2021-12-01T00:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1016/j.comcom.2021.09.009https://figshare.com/articles/journal_contribution/Towards_real-time_privacy-preserving_video_surveillance/24420310CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/244203102021-12-01T00:00:00Z |
| spellingShingle | Towards real-time privacy-preserving video surveillance Elmahdi Bentafat (16896405) Information and computing sciences Computer vision and multimedia computation Cybersecurity and privacy Video surveillance systems Privacy Face recognition License plate recognition Homomorphic encryption |
| status_str | publishedVersion |
| title | Towards real-time privacy-preserving video surveillance |
| title_full | Towards real-time privacy-preserving video surveillance |
| title_fullStr | Towards real-time privacy-preserving video surveillance |
| title_full_unstemmed | Towards real-time privacy-preserving video surveillance |
| title_short | Towards real-time privacy-preserving video surveillance |
| title_sort | Towards real-time privacy-preserving video surveillance |
| topic | Information and computing sciences Computer vision and multimedia computation Cybersecurity and privacy Video surveillance systems Privacy Face recognition License plate recognition Homomorphic encryption |