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Recent CVS providers use different machine learning techniques to improve their services (regarded as tasks) based on the uploaded video. However, uploading data to the CVS providers may cause severe privacy issues. Existing works that remove privacy information could not achieve a high tradeoff between data usability and privacy, because the importance of information varies with the task. In addition, it is challenging to design a real-time privacy protection mechanism, especially in resource-constrained smart cameras. In this work, we design a task-driven and efficient video privacy protection mechanism for a better tradeoff between privacy and data usability. We use Class Activation Mapping to protect privacy while preserving data usability. To improve the efficiency, we utilize the motion vector and residual matrix produced during video codec. Our work outperforms the region of interest\u2013based methods in data protection while preserving data usability. The attack accuracy drops 70%, while the task accuracy is comparable to those without protection (within \u00b1 4%). The average protection frame rate of the High Definition video can exceed 16 fps+ even on a CPU.<\/jats:p>","DOI":"10.1145\/3625825","type":"journal-article","created":{"date-parts":[[2023,10,2]],"date-time":"2023-10-02T08:24:49Z","timestamp":1696235089000},"page":"1-21","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":5,"title":["Efficient Task-driven Video Data Privacy Protection for Smart Camera Surveillance System"],"prefix":"10.1145","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0398-2631","authenticated-orcid":false,"given":"Zhiqiang","family":"Wang","sequence":"first","affiliation":[{"name":"University of Science and Technology of China, Hefei, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3340-8585","authenticated-orcid":false,"given":"Jiahui","family":"Hou","sequence":"additional","affiliation":[{"name":"University of Science and Technology of China, Hefei, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3603-3886","authenticated-orcid":false,"given":"Guangyu","family":"Wu","sequence":"additional","affiliation":[{"name":"University of Science and Technology of China, Hefei, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0018-0366","authenticated-orcid":false,"given":"Suyuan","family":"Liu","sequence":"additional","affiliation":[{"name":"University of Science and Technology of China, Hefei, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-6885-1185","authenticated-orcid":false,"given":"Puhan","family":"Luo","sequence":"additional","affiliation":[{"name":"University of Science and Technology of China, Hefei, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6070-6625","authenticated-orcid":false,"given":"Xiangyang","family":"Li","sequence":"additional","affiliation":[{"name":"University of Science and Technology of China, Hefei, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2024,5,11]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"publisher","DOI":"10.1109\/WACV.2018.00097"},{"key":"e_1_3_1_3_2","article-title":"Amadeus: Scalable, privacy-preserving live video analytics","author":"Dsouza Sandeep","year":"2020","unstructured":"Sandeep Dsouza, Victor Bahl, Lixiang Ao, and Landon P Cox. 2020. 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