{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T16:24:38Z","timestamp":1777652678858,"version":"3.51.4"},"publisher-location":"New York, NY, USA","reference-count":9,"publisher":"ACM","license":[{"start":{"date-parts":[[2023,6,18]],"date-time":"2023-06-18T00:00:00Z","timestamp":1687046400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["No. 62102094"],"award-info":[{"award-number":["No. 62102094"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["No. 62072115"],"award-info":[{"award-number":["No. 62072115"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"NIO"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2023,6,18]]},"DOI":"10.1145\/3581791.3597364","type":"proceedings-article","created":{"date-parts":[[2023,6,16]],"date-time":"2023-06-16T17:52:21Z","timestamp":1686937941000},"page":"557-558","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Poster: A Privacy-preserving Heart Rate Prediction System for Drivers in Connected Vehicles"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9934-2259","authenticated-orcid":false,"given":"Hui","family":"Ruan","sequence":"first","affiliation":[{"name":"Fudan University, Shanghai, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7942-8752","authenticated-orcid":false,"given":"Qingyuan","family":"Gong","sequence":"additional","affiliation":[{"name":"Fudan University, Shanghai, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4749-3060","authenticated-orcid":false,"given":"Yang","family":"Chen","sequence":"additional","affiliation":[{"name":"Fudan University, Shanghai, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-1934-8962","authenticated-orcid":false,"given":"Jiong","family":"Chen","sequence":"additional","affiliation":[{"name":"NIO, Shanghai, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4983-9352","authenticated-orcid":false,"given":"Ziyue","family":"Li","sequence":"additional","affiliation":[{"name":"University of Cologne, Cologne, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5945-9551","authenticated-orcid":false,"given":"Xiang","family":"Su","sequence":"additional","affiliation":[{"name":"Norwegian University of Science and Technology, Trondheim, Norway"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2023,6,18]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1093\/ije\/22.1.57"},{"key":"e_1_3_2_1_2_1","volume-title":"An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling. arXiv preprint arXiv:1803.01271","author":"Bai Shaojie","year":"2018","unstructured":"Shaojie Bai , J. Zico Kolter , and Vladlen Koltun . 2018. An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling. arXiv preprint arXiv:1803.01271 ( 2018 ). Shaojie Bai, J. Zico Kolter, and Vladlen Koltun. 2018. An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling. arXiv preprint arXiv:1803.01271 (2018)."},{"key":"e_1_3_2_1_3_1","volume-title":"Proc. of SIGIR.","author":"Lai Guokun","unstructured":"Guokun Lai , Wei-Cheng Chang , Yiming Yang , and et al. 2018. Modeling Long-and Short-Term Temporal Patterns with Deep Neural Networks . In Proc. of SIGIR. Guokun Lai, Wei-Cheng Chang, Yiming Yang, and et al. 2018. Modeling Long-and Short-Term Temporal Patterns with Deep Neural Networks. In Proc. of SIGIR."},{"key":"e_1_3_2_1_4_1","volume-title":"Proc. of ICLR.","author":"Li Junnan","year":"2021","unstructured":"Junnan Li , Pan Zhou , Caiming Xiong , and Steven Hoi . 2021 . Prototypical Contrastive Learning of Unsupervised Representations . In Proc. of ICLR. Junnan Li, Pan Zhou, Caiming Xiong, and Steven Hoi. 2021. Prototypical Contrastive Learning of Unsupervised Representations. In Proc. of ICLR."},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2020.02.042"},{"key":"e_1_3_2_1_6_1","volume-title":"Proc. of MobiSys.","author":"Mo Fan","unstructured":"Fan Mo , Hamed Haddadi , Kleomenis Katevas , and et al. 2021. PPFL: privacy-preserving federated learning with trusted execution environments . In Proc. of MobiSys. Fan Mo, Hamed Haddadi, Kleomenis Katevas, and et al. 2021. PPFL: privacy-preserving federated learning with trusted execution environments. In Proc. of MobiSys."},{"key":"e_1_3_2_1_7_1","volume-title":"Proc. of ICLR.","author":"Oreshkin Boris N.","year":"2020","unstructured":"Boris N. Oreshkin , Dmitri Carpov , Nicolas Chapados , and Yoshua Bengio . 2020 . N-BEATS: Neural basis expansion analysis for interpretable time series forecasting . In Proc. of ICLR. Boris N. Oreshkin, Dmitri Carpov, Nicolas Chapados, and Yoshua Bengio. 2020. N-BEATS: Neural basis expansion analysis for interpretable time series forecasting. In Proc. of ICLR."},{"key":"e_1_3_2_1_8_1","volume-title":"Proc. of AAAI.","author":"Yue Zhihan","unstructured":"Zhihan Yue , Yujing Wang , Juanyong Duan , and et al. 2022. TS2Vec: Towards Universal Representation of Time Series . In Proc. of AAAI. Zhihan Yue, Yujing Wang, Juanyong Duan, and et al. 2022. TS2Vec: Towards Universal Representation of Time Series. In Proc. of AAAI."},{"key":"e_1_3_2_1_9_1","volume-title":"Proc. of INFOCOM.","author":"Zhao Lingchen","unstructured":"Lingchen Zhao , Lihao Ni , Shengshan Hu , and et al. 2018. InPrivate Digging: Enabling Tree-based Distributed Data Mining with Differential Privacy . In Proc. of INFOCOM. Lingchen Zhao, Lihao Ni, Shengshan Hu, and et al. 2018. InPrivate Digging: Enabling Tree-based Distributed Data Mining with Differential Privacy. In Proc. of INFOCOM."}],"event":{"name":"MobiSys '23: 21st Annual International Conference on Mobile Systems, Applications and Services","location":"Helsinki Finland","acronym":"MobiSys '23","sponsor":["SIGMOBILE ACM Special Interest Group on Mobility of Systems, Users, Data and Computing","SIGOPS ACM Special Interest Group on Operating Systems"]},"container-title":["Proceedings of the 21st Annual International Conference on Mobile Systems, Applications and Services"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3581791.3597364","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T16:36:31Z","timestamp":1750178191000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3581791.3597364"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,6,18]]},"references-count":9,"alternative-id":["10.1145\/3581791.3597364","10.1145\/3581791"],"URL":"https:\/\/doi.org\/10.1145\/3581791.3597364","relation":{},"subject":[],"published":{"date-parts":[[2023,6,18]]},"assertion":[{"value":"2023-06-18","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}