{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,19]],"date-time":"2024-09-19T16:28:52Z","timestamp":1726763332029},"reference-count":30,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2023,8,1]],"date-time":"2023-08-01T00:00:00Z","timestamp":1690848000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2024,5,25]],"date-time":"2024-05-25T00:00:00Z","timestamp":1716595200000},"content-version":"am","delay-in-days":298,"URL":"http:\/\/www.elsevier.com\/open-access\/userlicense\/1.0\/"},{"start":{"date-parts":[[2023,8,1]],"date-time":"2023-08-01T00:00:00Z","timestamp":1690848000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2023,8,1]],"date-time":"2023-08-01T00:00:00Z","timestamp":1690848000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2023,8,1]],"date-time":"2023-08-01T00:00:00Z","timestamp":1690848000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2023,8,1]],"date-time":"2023-08-01T00:00:00Z","timestamp":1690848000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2023,8,1]],"date-time":"2023-08-01T00:00:00Z","timestamp":1690848000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100011554","name":"Conseil D\u00e9partemental de l'Ard\u00e8che","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100011554","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Computers & Security"],"published-print":{"date-parts":[[2023,8]]},"DOI":"10.1016\/j.cose.2023.103211","type":"journal-article","created":{"date-parts":[[2023,3,28]],"date-time":"2023-03-28T17:00:02Z","timestamp":1680022802000},"page":"103211","update-policy":"http:\/\/dx.doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":6,"special_numbering":"C","title":["Privacy-Preserving federated learning: An application for big data load forecast in buildings"],"prefix":"10.1016","volume":"131","author":[{"ORCID":"http:\/\/orcid.org\/0000-0002-6017-0912","authenticated-orcid":false,"given":"Maysaa","family":"Khalil","sequence":"first","affiliation":[]},{"given":"Moez","family":"Esseghir","sequence":"additional","affiliation":[]},{"given":"Leila Merghem","family":"Boulahia","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"key":"10.1016\/j.cose.2023.103211_bib0001","series-title":"Proceedings of the 2016 ACM SIGSAC conference on computer and communications security","first-page":"308","article-title":"Deep learning with differential privacy","author":"Abadi","year":"2016"},{"key":"10.1016\/j.cose.2023.103211_bib0002","unstructured":"Amiri, M. M., Gunduz, D., Kulkarni, S. R., Poor, H. V., 2020. Federated learning with quantized global model updates. Arxiv:2006.10672"},{"issue":"7","key":"10.1016\/j.cose.2023.103211_bib0003","doi-asserted-by":"crossref","first-page":"5827","DOI":"10.1109\/JIOT.2019.2952146","article-title":"Local differential privacy for deep learning","volume":"7","author":"Arachchige","year":"2019","journal-title":"IEEE Internet Things J."},{"key":"10.1016\/j.cose.2023.103211_bib0004","series-title":"International Conference on Machine Learning","first-page":"560","article-title":"Signsgd: compressed optimisation for non-convex problems","author":"Bernstein","year":"2018"},{"key":"10.1016\/j.cose.2023.103211_sbref0004","series-title":"Proceedings of the 35th International Conference on Machine Learning","first-page":"560","article-title":"signSGD: compressed optimisation for non-convex problems","author":"Bernstein","year":"2018"},{"issue":"7","key":"10.1016\/j.cose.2023.103211_bib0006","doi-asserted-by":"crossref","first-page":"1636","DOI":"10.3390\/en11071636","article-title":"Optimal deep learning lstm model for electric load forecasting using feature selection and genetic algorithm: comparison with machine learning approaches","volume":"11","author":"Bouktif","year":"2018","journal-title":"Energies"},{"key":"10.1016\/j.cose.2023.103211_bib0007","doi-asserted-by":"crossref","first-page":"101824","DOI":"10.1016\/j.cose.2020.101824","article-title":"Eastfly: efficient and secure ternary federated learning","volume":"94","author":"Dong","year":"2020","journal-title":"Comput. Secur."},{"key":"10.1016\/j.cose.2023.103211_bib0008","doi-asserted-by":"crossref","first-page":"102199","DOI":"10.1016\/j.cose.2021.102199","article-title":"Privacy-preserving and communication-efficient federated learning in internet of things","volume":"103","author":"Fang","year":"2021","journal-title":"Comput. Secur."},{"key":"10.1016\/j.cose.2023.103211_bib0009","doi-asserted-by":"crossref","first-page":"101889","DOI":"10.1016\/j.cose.2020.101889","article-title":"Highly efficient federated learning with strong privacy preservation in cloud computing","volume":"96","author":"Fang","year":"2020","journal-title":"Comput. Secur."},{"key":"10.1016\/j.cose.2023.103211_bib0010","series-title":"International Conference on Artificial Intelligence and Statistics","first-page":"2350","article-title":"Federated learning with compression: unified analysis and sharp guarantees","author":"Haddadpour","year":"2021"},{"key":"10.1016\/j.cose.2023.103211_bib0011","series-title":"2019 IEEE Global Communications Conference (GLOBECOM)","first-page":"1","article-title":"Smart grid energy management using rnn-lstm: a deep learning-based approach","author":"Kaur","year":"2019"},{"key":"10.1016\/j.cose.2023.103211_bib0012","doi-asserted-by":"crossref","first-page":"102464","DOI":"10.1016\/j.cose.2021.102464","article-title":"A survey of differential privacy-based techniques and their applicability to location-based services","author":"Kim","year":"2021","journal-title":"Comput. Secur."},{"issue":"10","key":"10.1016\/j.cose.2023.103211_bib0013","doi-asserted-by":"crossref","first-page":"9895","DOI":"10.1109\/JIOT.2020.2988033","article-title":"Multiagent ddpg-based deep learning for smart ocean federated learning iot networks","volume":"7","author":"Kwon","year":"2020","journal-title":"IEEE Internet Things J."},{"key":"10.1016\/j.cose.2023.103211_bib0014","series-title":"Artificial Intelligence and Statistics","first-page":"1273","article-title":"Communication-efficient learning of deep networks from decentralized data","author":"McMahan","year":"2017"},{"key":"10.1016\/j.cose.2023.103211_bib0015","unstructured":"Melis, L., Song, C., De Cristofaro, E., Shmatikov, V., 2018. Inference attacks against collaborative learning. Arxiv:1805.0404913."},{"issue":"10","key":"10.1016\/j.cose.2023.103211_bib0016","doi-asserted-by":"crossref","first-page":"1094","DOI":"10.1109\/71.963420","article-title":"The power of two choices in randomized load balancing","volume":"12","author":"Mitzenmacher","year":"2001","journal-title":"IEEE Trans. Parallel Distrib. Syst."},{"key":"10.1016\/j.cose.2023.103211_bib0017","series-title":"GLOBECOM 2020 - 2020 IEEE Global Communications Conference","first-page":"1","article-title":"Residential appliance-level load forecasting with deep learning","author":"Razghandi","year":"2020"},{"key":"10.1016\/j.cose.2023.103211_bib0018","series-title":"International Conference on Artificial Intelligence and Statistics","first-page":"2021","article-title":"Fedpaq: a communication-efficient federated learning method with periodic averaging and quantization","author":"Reisizadeh","year":"2020"},{"issue":"10","key":"10.1016\/j.cose.2023.103211_bib0019","doi-asserted-by":"crossref","first-page":"2430","DOI":"10.1109\/JSAC.2020.3000372","article-title":"Analyzing user-level privacy attack against federated learning","volume":"38","author":"Song","year":"2020","journal-title":"IEEE J. Sel. Areas Commun."},{"key":"10.1016\/j.cose.2023.103211_sbref0018","article-title":"Dataport: the worlds largest energy data resource","author":"Street","year":"2015","journal-title":"Pecan Street Inc"},{"key":"10.1016\/j.cose.2023.103211_bib0021","doi-asserted-by":"crossref","first-page":"102402","DOI":"10.1016\/j.cose.2021.102402","article-title":"Privacy preservation in federated learning: an insightful survey from the gdpr perspective","volume":"110","author":"Truong","year":"2021","journal-title":"Comput. Secur."},{"key":"10.1016\/j.cose.2023.103211_bib0022","first-page":"3152676","article-title":"The eu general data protection regulation (gdpr)","volume":"10","author":"Voigt","year":"2017","journal-title":"A Practical Guide, 1st Ed., Cham: Springer International Publishing"},{"key":"10.1016\/j.cose.2023.103211_bib0023","series-title":"2021 IEEE 18th Annual Consumer Communications & Networking Conference (CCNC)","first-page":"1","article-title":"Label leakage from gradients in distributed machine learning","author":"Wainakh","year":"2021"},{"key":"10.1016\/j.cose.2023.103211_bib0024","series-title":"2018 IEEE Global Communications Conference (GLOBECOM)","first-page":"1","article-title":"Data-driven optimization for utility providers with differential privacy of users\u2019 energy profile","author":"Wang","year":"2018"},{"key":"10.1016\/j.cose.2023.103211_bib0025","series-title":"IEEE INFOCOM 2019-IEEE Conference on Computer Communications","first-page":"2512","article-title":"Beyond inferring class representatives: User-level privacy leakage from federated learning","author":"Wang","year":"2019"},{"key":"10.1016\/j.cose.2023.103211_bib0026","unstructured":"Wei, W., Liu, L., Loper, M., Chow, K.-H., Gursoy, M. E., Truex, S., Wu, Y., 2020. A framework for evaluating gradient leakage attacks in federated learning. Arxiv:2004.10397"},{"key":"10.1016\/j.cose.2023.103211_bib0027","series-title":"Speech at Future of Privacy Forum by Commissioner of US Federal Trade Commission (FTC)","article-title":"A defining moment for privacy: the time is ripe for federal privacy legislation","volume":"Vol.\u00a06","author":"Wilson","year":"2020"},{"key":"10.1016\/j.cose.2023.103211_bib0028","article-title":"Energy efficient federated learning over wireless communication networks","author":"Yang","year":"2020","journal-title":"IEEE Trans. Wirel. Commun."},{"key":"10.1016\/j.cose.2023.103211_bib0029","series-title":"2019 18th IEEE International Conference On Trust, Security And Privacy In Computing And Communications\/13th IEEE International Conference On Big Data Science And Engineering (TrustCom\/BigDataSE)","first-page":"374","article-title":"Poisoning attack in federated learning using generative adversarial nets","author":"Zhang","year":"2019"},{"issue":"3","key":"10.1016\/j.cose.2023.103211_bib0030","doi-asserted-by":"crossref","first-page":"310","DOI":"10.1177\/0143624417740858","article-title":"Short-term load forecasting coupled with weather profile generation methodology","volume":"39","author":"Zhu","year":"2018","journal-title":"Build. Serv. Eng. Res. Technol."}],"container-title":["Computers & Security"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0167404823001219?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0167404823001219?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2024,3,29]],"date-time":"2024-03-29T00:55:12Z","timestamp":1711673712000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0167404823001219"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,8]]},"references-count":30,"alternative-id":["S0167404823001219"],"URL":"https:\/\/doi.org\/10.1016\/j.cose.2023.103211","relation":{},"ISSN":["0167-4048"],"issn-type":[{"value":"0167-4048","type":"print"}],"subject":[],"published":{"date-parts":[[2023,8]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Privacy-Preserving federated learning: An application for big data load forecast in buildings","name":"articletitle","label":"Article Title"},{"value":"Computers & Security","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.cose.2023.103211","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2023 Elsevier Ltd. All rights reserved.","name":"copyright","label":"Copyright"}],"article-number":"103211"}}