{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,14]],"date-time":"2026-03-14T18:00:51Z","timestamp":1773511251462,"version":"3.50.1"},"reference-count":92,"publisher":"Association for Computing Machinery (ACM)","issue":"2","license":[{"start":{"date-parts":[[2023,4,13]],"date-time":"2023-04-13T00:00:00Z","timestamp":1681344000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Priv. Secur."],"published-print":{"date-parts":[[2023,5,31]]},"abstract":"<jats:p>\n            Voice user interfaces and digital assistants are rapidly entering our lives and becoming singular touch points spanning our devices. These\n            <jats:italic>always-on<\/jats:italic>\n            services capture and transmit our audio data to powerful cloud services for further processing and subsequent actions. Our voices and raw audio signals collected through these devices contain a host of sensitive paralinguistic information that is transmitted to service providers regardless of deliberate or false triggers. As our emotional patterns and sensitive attributes like our identity, gender, and well-being are easily inferred using deep acoustic models, we encounter a new generation of privacy risks by using these services. One approach to mitigate the risk of paralinguistic-based privacy breaches is to exploit a combination of cloud-based processing with privacy-preserving, on-device paralinguistic information learning and filtering before transmitting voice data.\n          <\/jats:p>\n          <jats:p>\n            In this article we introduce\n            <jats:italic>EDGY<\/jats:italic>\n            , a configurable, lightweight, disentangled representation learning framework that transforms and filters high-dimensional voice data to identify and contain sensitive attributes at the edge prior to offloading to the cloud. We evaluate EDGY\u2019s on-device performance and explore optimization techniques, including model quantization and knowledge distillation, to enable private, accurate, and efficient representation learning on resource-constrained devices. Our results show that EDGY runs in tens of milliseconds with 0.2% relative improvement in \u201czero-shot\u201d ABX score or minimal performance penalties of approximately 5.95% word error rate (WER) in learning linguistic representations from raw voice signals, using a CPU and a single-core ARM processor without specialized hardware.\n          <\/jats:p>","DOI":"10.1145\/3570161","type":"journal-article","created":{"date-parts":[[2022,11,3]],"date-time":"2022-11-03T11:24:37Z","timestamp":1667474677000},"page":"1-27","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":8,"title":["Paralinguistic Privacy Protection at the Edge"],"prefix":"10.1145","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4436-751X","authenticated-orcid":false,"given":"Ranya","family":"Aloufi","sequence":"first","affiliation":[{"name":"Imperial College London, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8357-9059","authenticated-orcid":false,"given":"Hamed","family":"Haddadi","sequence":"additional","affiliation":[{"name":"Imperial College London, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1993-4482","authenticated-orcid":false,"given":"David","family":"Boyle","sequence":"additional","affiliation":[{"name":"Imperial College London, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2023,4,13]]},"reference":[{"key":"e_1_3_2_2_2","article-title":"The faults in our ASRs: An overview of attacks against automatic speech recognition and speaker identification systems","author":"Abdullah Hadi","year":"2020","unstructured":"Hadi Abdullah, Kevin Warren, Vincent Bindschaedler, Nicolas Papernot, and Patrick Traynor. 2020. 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