{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,5]],"date-time":"2026-08-05T22:31:06Z","timestamp":1785969066631,"version":"3.56.0"},"reference-count":33,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100008871","name":"Yunnan Provincial Science and Technology Department","doi-asserted-by":"publisher","award":["202502AD080010"],"award-info":[{"award-number":["202502AD080010"]}],"id":[{"id":"10.13039\/501100008871","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100008871","name":"Yunnan Provincial Science and Technology Department","doi-asserted-by":"publisher","award":["202402AD080003"],"award-info":[{"award-number":["202402AD080003"]}],"id":[{"id":"10.13039\/501100008871","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100008871","name":"Yunnan Provincial Science and Technology Department","doi-asserted-by":"publisher","award":["2025J0101"],"award-info":[{"award-number":["2025J0101"]}],"id":[{"id":"10.13039\/501100008871","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Journal of Information Security and Applications"],"published-print":{"date-parts":[[2026,11]]},"DOI":"10.1016\/j.jisa.2026.104562","type":"journal-article","created":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T20:58:23Z","timestamp":1782939503000},"page":"104562","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Direction-aware local differential privacy for federated learning"],"prefix":"10.1016","volume":"102","author":[{"given":"Shouguo","family":"Tang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bangguo","family":"Wu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yingna","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lulu","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haoheng","family":"Yuan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"10","key":"10.1016\/j.jisa.2026.104562_bib0001","first-page":"2130","article-title":"Survey of data privacy security based on GDPR","volume":"59","author":"Zhao","year":"2022","journal-title":"J Comput Res Dev"},{"key":"10.1016\/j.jisa.2026.104562_bib0002","article-title":"The age of analytics: competing in a data-driven world","year":"2016","journal-title":"Research Report"},{"key":"10.1016\/j.jisa.2026.104562_bib0003","unstructured":"Kone\u010dn\u00fd J., McMahan H.B., Yu F.X., Richt\u00e1rik P., Suresh A.T., Bacon D.. Federated learning: strategies for improving communication efficiency. 2016. arXiv preprint arXiv: 161005492."},{"key":"10.1016\/j.jisa.2026.104562_bib0004","first-page":"16937","article-title":"Inverting gradients\u2013how easy is it to break privacy in federated learning?","volume":"33","author":"Geiping","year":"2020","journal-title":"Adv Neural Inf Process Syst"},{"key":"10.1016\/j.jisa.2026.104562_bib0005","series-title":"2020 USENIX annual technical conference (USENIX ATC 20)","first-page":"493","article-title":"{BatchCrypt}: efficient homomorphic encryption for {Cross-Silo} federated learning","author":"Zhang","year":"2020"},{"key":"10.1016\/j.jisa.2026.104562_bib0006","series-title":"Proceedings of the 2017 ACM SIGSAC conference on computer and communications security","first-page":"1175","article-title":"Practical secure aggregation for privacy-preserving machine learning","author":"Bonawitz","year":"2017"},{"key":"10.1016\/j.jisa.2026.104562_bib0007","series-title":"Proceedings of the 2016\u202fACM 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.jisa.2026.104562_bib0008","series-title":"Proceedings of the 2018 international conference on management of data","first-page":"1655","article-title":"Privacy at scale: local differential privacy in practice","author":"Cormode","year":"2018"},{"key":"10.1016\/j.jisa.2026.104562_bib0009","series-title":"Proceedings of the third ACM international workshop on edge systems, analytics and networking","first-page":"61","article-title":"LDP-Fed: federated learning with local differential privacy","author":"Truex","year":"2020"},{"key":"10.1016\/j.jisa.2026.104562_bib0010","doi-asserted-by":"crossref","unstructured":"Sun L., Qian J., Chen X.. LDP-FL: practical private aggregation in federated learning with local differential privacy. 2020. arXiv preprint arXiv: 200715789.","DOI":"10.24963\/ijcai.2021\/217"},{"key":"10.1016\/j.jisa.2026.104562_bib0011","doi-asserted-by":"crossref","first-page":"3454","DOI":"10.1109\/TIFS.2020.2988575","article-title":"Federated learning with differential privacy: algorithms and performance analysis","volume":"15","author":"Wei","year":"2020","journal-title":"IEEE Trans Inf Forensics Secur"},{"key":"10.1016\/j.jisa.2026.104562_bib0012","unstructured":"Geyer R.C., Klein T., Nabi M.. Differentially private federated learning: a client level perspective. 2017. arXiv preprint arXiv: 171207557."},{"key":"10.1016\/j.jisa.2026.104562_bib0013","unstructured":"McMahan H.B., Ramage D., Talwar K., Zhang L.. Learning differentially private recurrent language models. 2017. arXiv preprint arXiv: 171006963."},{"key":"10.1016\/j.jisa.2026.104562_bib0014","series-title":"Proceedings of the AAAI conference on artificial intelligence","first-page":"9312","article-title":"Tempered sigmoid activations for deep learning with differential privacy","volume":"vol. 35","author":"Papernot","year":"2021"},{"key":"10.1016\/j.jisa.2026.104562_bib0015","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2024.126228","article-title":"Privacy-preserving federated learning based on noise addition","volume":"267","author":"Wu","year":"2025","journal-title":"Expert Syst Appl"},{"issue":"5","key":"10.1016\/j.jisa.2026.104562_bib0016","doi-asserted-by":"crossref","first-page":"2599","DOI":"10.1109\/TMC.2021.3130060","article-title":"Differentially-private deep learning with directional noise","volume":"22","author":"Xiang","year":"2021","journal-title":"IEEE Trans Mob Comput"},{"key":"10.1016\/j.jisa.2026.104562_bib0017","unstructured":"Yan N., Wang K., Zhi K., Pan C., Chai K.K., Poor H.V.. Toward secure and private over-the-air federated learning. 2022. arXiv preprint arXiv: 221007669."},{"key":"10.1016\/j.jisa.2026.104562_bib0018","series-title":"2019 IEEE symposium on security and privacy (SP)","first-page":"691","article-title":"Exploiting unintended feature leakage in collaborative learning","author":"Melis","year":"2019"},{"key":"10.1016\/j.jisa.2026.104562_bib0019","first-page":"691","article-title":"Deep leakage from gradients","volume":"32","author":"Zhu","year":"2019","journal-title":"Adv Neural Inf Process Syst"},{"key":"10.1016\/j.jisa.2026.104562_bib0020","article-title":"Private collaborative neural network learning","author":"Chase","year":"2017","journal-title":"Cryptol ePrint Arch"},{"key":"10.1016\/j.jisa.2026.104562_bib0021","series-title":"2021 IEEE 41st international conference on distributed computing systems (ICDCS)","first-page":"797","article-title":"Gradient-leakage resilient federated learning","author":"Wei","year":"2021"},{"key":"10.1016\/j.jisa.2026.104562_bib0022","unstructured":"Naseri M., Hayes J., De Cristofaro E.. Local and central differential privacy for robustness and privacy in federated learning. 2020. arXiv preprint arXiv: 200903561."},{"key":"10.1016\/j.jisa.2026.104562_bib0023","series-title":"2014 IEEE 55th annual symposium on foundations of computer science","first-page":"464","article-title":"Private empirical risk minimization: efficient algorithms and tight error bounds","author":"Bassily","year":"2014"},{"key":"10.1016\/j.jisa.2026.104562_bib0024","unstructured":"Tramer F., Boneh D.. Differentially private learning needs better features (or much more data). 2020. arXiv preprint arXiv: 201111660."},{"issue":"9","key":"10.1016\/j.jisa.2026.104562_bib0025","doi-asserted-by":"crossref","first-page":"1770","DOI":"10.1109\/TKDE.2018.2805356","article-title":"Privacy enhanced matrix factorization for recommendation with local differential privacy","volume":"30","author":"Shin","year":"2018","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"10.1016\/j.jisa.2026.104562_bib0026","series-title":"International conference on database systems for advanced applications","first-page":"485","article-title":"FedSel: federated SGD under local differential privacy with top-k dimension selection","author":"Liu","year":"2020"},{"issue":"1","key":"10.1016\/j.jisa.2026.104562_bib0027","doi-asserted-by":"crossref","first-page":"910","DOI":"10.1109\/TII.2022.3161517","article-title":"Boosting accuracy of differentially private federated learning in industrial IoT with sparse responses","volume":"19","author":"Cui","year":"2022","journal-title":"IEEE Trans Ind Inf"},{"key":"10.1016\/j.jisa.2026.104562_bib0028","series-title":"2021 IEEE European symposium on security and privacy (EuroS&P)","first-page":"304","article-title":"Compression boosts differentially private federated learning","author":"Kerkouche","year":"2021"},{"key":"10.1016\/j.jisa.2026.104562_bib0029","series-title":"2021 60th IEEE conference on decision and control (CDC)","first-page":"1695","article-title":"Gradient sparsification can improve performance of differentially-private convex machine learning","author":"Farokhi","year":"2021"},{"key":"10.1016\/j.jisa.2026.104562_bib0030","series-title":"Theory of cryptography conference","first-page":"265","article-title":"Calibrating noise to sensitivity in private data analysis","author":"Dwork","year":"2006"},{"key":"10.1016\/j.jisa.2026.104562_bib0031","series-title":"48th annual IEEE symposium on foundations of computer science (FOCS\u201907)","first-page":"94","article-title":"Mechanism design via differential privacy","author":"McSherry","year":"2007"},{"key":"10.1016\/j.jisa.2026.104562_bib0032","series-title":"International conference on machine learning","first-page":"12208","article-title":"Large scale private learning via low-rank reparametrization","author":"Yu","year":"2021"},{"key":"10.1016\/j.jisa.2026.104562_bib0033","series-title":"Artificial intelligence and statistics","first-page":"1273","article-title":"Communication-efficient learning of deep networks from decentralized data","author":"McMahan","year":"2017"}],"container-title":["Journal of Information Security and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2214212626001924?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2214212626001924?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,8,5]],"date-time":"2026-08-05T20:55:36Z","timestamp":1785963336000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S2214212626001924"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,11]]},"references-count":33,"alternative-id":["S2214212626001924"],"URL":"https:\/\/doi.org\/10.1016\/j.jisa.2026.104562","relation":{},"ISSN":["2214-2126"],"issn-type":[{"value":"2214-2126","type":"print"}],"subject":[],"published":{"date-parts":[[2026,11]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Direction-aware local differential privacy for federated learning","name":"articletitle","label":"Article Title"},{"value":"Journal of Information Security and Applications","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.jisa.2026.104562","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"104562"}}