{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,27]],"date-time":"2026-05-27T12:44:27Z","timestamp":1779885867552,"version":"3.53.1"},"reference-count":56,"publisher":"Association for Computing Machinery (ACM)","issue":"8","content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Multimedia Comput. Commun. Appl."],"published-print":{"date-parts":[[2025,8,31]]},"abstract":"<jats:p>\n            Cloud-based video analytics services have been widely employed to support various real-world surveillance and monitoring applications while bearing the risk of disclosing sensitive visuals. The state-of-the-art (SoTA) solution has explored the feasibility of applying cryptographic techniques for privacy-preserving video analytics, but unfortunately incurs high computation and communication burdens on the end users. In this article, we propose\n            <jats:monospace>Pri3D<\/jats:monospace>\n            , an efficient privacy-preserving video analytics system performed over two distributed clouds.\n            <jats:monospace>Pri3D<\/jats:monospace>\n            flexibly combines additive and multiplicative secret sharing techniques to free end devices and facilitate on-premise analytics services. Particularly, targeting the mainstream 3D convolutional neural network (CNN) pipeline,\n            <jats:monospace>Pri3D<\/jats:monospace>\n            securely accomplishes the non-linear operations (e.g., ReLU and max pooling) in merely two interaction rounds, owing to the novel design of the bi-directional transforming protocols for different modalities of secret sharing. To further optimize the latency and bandwidth confronted with large amounts of video data,\n            <jats:inline-formula content-type=\"math\/tex\">\n              <jats:tex-math notation=\"LaTeX\" version=\"MathJax\">\\(\\textsf{AS2MS}^{++}\\)<\/jats:tex-math>\n            <\/jats:inline-formula>\n            and\n            <jats:inline-formula content-type=\"math\/tex\">\n              <jats:tex-math notation=\"LaTeX\" version=\"MathJax\">\\(\\textsf{MS2AS}^{++}\\)<\/jats:tex-math>\n            <\/jats:inline-formula>\n            are proposed by subtly utilizing randomization factors and pre-encrypted nonce. With the transforming protocols, a series of privacy-preserving layer protocols are devised and tailored to build up the privacy-preserving analytics pipeline. Theoretical analysis shows that\n            <jats:monospace>Pri3D<\/jats:monospace>\n            can effectively fulfill the desired privacy requirements. Extensive evaluations demonstrate that\n            <jats:monospace>Pri3D<\/jats:monospace>\n            provides up to 11.85\n            <jats:inline-formula content-type=\"math\/tex\">\n              <jats:tex-math notation=\"LaTeX\" version=\"MathJax\">\\(\\times\\)<\/jats:tex-math>\n            <\/jats:inline-formula>\n            speed boost and 14.86\n            <jats:inline-formula content-type=\"math\/tex\">\n              <jats:tex-math notation=\"LaTeX\" version=\"MathJax\">\\(\\times\\)<\/jats:tex-math>\n            <\/jats:inline-formula>\n            communication reduction compared to the SoTA work, while it is sufficiently efficient for working on resource-constrained devices.\n          <\/jats:p>","DOI":"10.1145\/3744248","type":"journal-article","created":{"date-parts":[[2025,6,10]],"date-time":"2025-06-10T11:37:36Z","timestamp":1749555456000},"page":"1-29","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Efficient Privacy-Preserving Video Analytics via Share\u00a0Transforming in Distributed Clouds"],"prefix":"10.1145","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8206-5910","authenticated-orcid":false,"given":"Tengfei","family":"Zheng","sequence":"first","affiliation":[{"name":"Phytium Research Institute, Phytium Technology Co., Ltd., Changsha, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6986-5174","authenticated-orcid":false,"given":"Bo","family":"Wang","sequence":"additional","affiliation":[{"name":"Phytium Research Institute, Phytium Technology Co., Ltd., Changsha, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-3482-3384","authenticated-orcid":false,"given":"Gen","family":"Li","sequence":"additional","affiliation":[{"name":"Phytium Research Institute, Phytium Technology Co., Ltd., Changsha, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4382-4942","authenticated-orcid":false,"given":"Yuxing","family":"Tang","sequence":"additional","affiliation":[{"name":"Phytium Research Institute, Phytium Technology Co., Ltd., Changsha, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-2121-0073","authenticated-orcid":false,"given":"Qiang","family":"Dou","sequence":"additional","affiliation":[{"name":"Phytium Research Institute, Phytium Technology Co., Ltd., Changsha, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2025,8,12]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1177\/1475921710373290"},{"key":"e_1_3_2_3_2","first-page":"110","volume-title":"IEEE\/ACM Symposium on Edge Computing","author":"Jain Samvit","year":"2020","unstructured":"Samvit Jain, Xun Zhang, Yuhao Zhou, Ganesh Ananthanarayanan, Junchen Jiang, Yuanchao Shu, Paramvir Bahl, and Joseph Gonzalez. 2020. 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