乐胖代购免代理版

{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,4]],"date-time":"2024-09-04T06:41:55Z","timestamp":1725432115610},"reference-count":40,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2024,5,1]],"date-time":"2024-05-01T00:00:00Z","timestamp":1714521600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2024,5,1]],"date-time":"2024-05-01T00:00:00Z","timestamp":1714521600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2024,5,1]],"date-time":"2024-05-01T00:00:00Z","timestamp":1714521600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2024,5,1]],"date-time":"2024-05-01T00:00:00Z","timestamp":1714521600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2024,5,1]],"date-time":"2024-05-01T00:00:00Z","timestamp":1714521600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2024,5,1]],"date-time":"2024-05-01T00:00:00Z","timestamp":1714521600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Future Generation Computer Systems"],"published-print":{"date-parts":[[2024,5]]},"DOI":"10.1016\/j.future.2023.12.030","type":"journal-article","created":{"date-parts":[[2023,12,30]],"date-time":"2023-12-30T16:10:48Z","timestamp":1703952648000},"page":"151-159","update-policy":"http:\/\/dx.doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":3,"special_numbering":"C","title":["FLPM: A property modification scheme for data protection in federated learning"],"prefix":"10.1016","volume":"154","author":[{"given":"Shuo","family":"Xu","sequence":"first","affiliation":[]},{"given":"Hui","family":"Xia","sequence":"additional","affiliation":[]},{"given":"Peishun","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Rui","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Hao","family":"Chi","sequence":"additional","affiliation":[]},{"given":"Wei","family":"Gao","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"key":"10.1016\/j.future.2023.12.030_b1","series-title":"Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, AISTATS, 54","first-page":"1273","article-title":"Communication-efficient learning of deep networks from decentralized data","author":"McMahan","year":"2017"},{"key":"10.1016\/j.future.2023.12.030_b2","series-title":"19th Annual IEEE International Conference on Sensing, Communication, and Networking, SECON","first-page":"325","article-title":"HPFL-CN: communication-efficient hierarchical personalized federated edge learning via complex network feature clustering","author":"Li","year":"2022"},{"key":"10.1016\/j.future.2023.12.030_b3","series-title":"19th Annual IEEE International Conference on Sensing, Communication, and Networking, SECON","first-page":"199","article-title":"Fedmix: A sybil attack detection system considering cross-layer information fusion and privacy protection","author":"Zhao","year":"2022"},{"key":"10.1016\/j.future.2023.12.030_b4","doi-asserted-by":"crossref","first-page":"148","DOI":"10.1016\/j.inffus.2022.09.011","article-title":"Survey on federated learning threats: Concepts, taxonomy on attacks and defences, experimental study and challenges","volume":"90","author":"Barroso","year":"2023","journal-title":"Inf. Fusion"},{"key":"10.1016\/j.future.2023.12.030_b5","series-title":"Proceedings of the 22nd ACM SIGSAC Conference on Computer and Communications Security","first-page":"1310","article-title":"Privacy-preserving deep learning","author":"Shokri","year":"2015"},{"key":"10.1016\/j.future.2023.12.030_b6","doi-asserted-by":"crossref","DOI":"10.1016\/j.sysarc.2022.102418","article-title":"Hybrid differential privacy based federated learning for internet of things","volume":"124","author":"Liu","year":"2022","journal-title":"J. Syst. Archit."},{"key":"10.1016\/j.future.2023.12.030_b7","article-title":"On data banks and privacy homomorphisms","author":"Rivest","year":"1978","journal-title":"Found. Secure Compuation"},{"issue":"5","key":"10.1016\/j.future.2023.12.030_b8","doi-asserted-by":"crossref","first-page":"968","DOI":"10.1109\/JSAC.2020.2980802","article-title":"Privacy-preserved data sharing towards multiple parties in industrial iots","volume":"38","author":"Zheng","year":"2020","journal-title":"IEEE J. Sel. Areas Commun."},{"issue":"4","key":"10.1016\/j.future.2023.12.030_b9","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1109\/MIS.2020.2988525","article-title":"A secure federated transfer learning framework","volume":"35","author":"Liu","year":"2020","journal-title":"IEEE Intell. Syst."},{"key":"10.1016\/j.future.2023.12.030_b10","series-title":"When relaxations go bad: differentially-private machine learning","author":"Jayaraman","year":"2019"},{"key":"10.1016\/j.future.2023.12.030_b11","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.future.2023.12.030_b12","series-title":"34th IEEE\/ACM International Conference on Automated Software Engineering, ASE","first-page":"797","article-title":"Property inference for deep neural networks","author":"Gopinath","year":"2019"},{"key":"10.1016\/j.future.2023.12.030_b13","series-title":"Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security, CCS","first-page":"619","article-title":"Property inference attacks on fully connected neural networks using permutation invariant representations","author":"Ganju","year":"2018"},{"key":"10.1016\/j.future.2023.12.030_b14","series-title":"CCS \u201921: 2021 ACM SIGSAC Conference on Computer and Communications Security","first-page":"2113","article-title":"Unleashing the tiger: Inference attacks on split learning","author":"Pasquini","year":"2021"},{"key":"10.1016\/j.future.2023.12.030_b15","doi-asserted-by":"crossref","DOI":"10.1016\/j.cose.2023.103297","article-title":"A comprehensive review on deep learning algorithms: Security and privacy issues","volume":"131","author":"Tayyab","year":"2023","journal-title":"Comput. Secur."},{"key":"10.1016\/j.future.2023.12.030_b16","series-title":"Badnets: Identifying vulnerabilities in the machine learning model supply chain","author":"Gu","year":"2017"},{"key":"10.1016\/j.future.2023.12.030_b17","series-title":"The Thirty-Fourth AAAI Conference on Artificial Intelligence, AAAI 2020, the Thirty-Second Innovative Applications of Artificial Intelligence Conference","first-page":"11957","article-title":"Hidden trigger backdoor attacks","author":"Saha","year":"2020"},{"key":"10.1016\/j.future.2023.12.030_b18","series-title":"19th Annual IEEE International Conference on Sensing, Communication, and Networking, SECON","first-page":"416","article-title":"Assisting backdoor federated learning with whole population knowledge alignment in mobile edge computing","author":"Liu","year":"2022"},{"issue":"4","key":"10.1016\/j.future.2023.12.030_b19","first-page":"577","article-title":"Collective data-sanitization for preventing sensitive information inference attacks in social networks","volume":"15","author":"Cai","year":"2018","journal-title":"IEEE Trans. Dependable Secur. Comput."},{"key":"10.1016\/j.future.2023.12.030_b20","series-title":"Backdoor attack in the physical world","author":"Li","year":"2021"},{"key":"10.1016\/j.future.2023.12.030_b21","series-title":"2nd International Conference on Learning Representations","article-title":"Auto-encoding variational bayes","author":"Kingma","year":"2014"},{"issue":"8","key":"10.1016\/j.future.2023.12.030_b22","doi-asserted-by":"crossref","first-page":"2002","DOI":"10.1007\/s11263-020-01308-z","article-title":"A survey of deep facial attribute analysis","volume":"128","author":"Zheng","year":"2020","journal-title":"Int. J. Comput. Vis."},{"key":"10.1016\/j.future.2023.12.030_b23","series-title":"Trends and Topics in Computer Vision - ECCV 2010 Workshops, Vol. 6553","first-page":"1","article-title":"Attribute learning in large-scale datasets","author":"Russakovsky","year":"2010"},{"issue":"1\u20132","key":"10.1016\/j.future.2023.12.030_b24","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1561\/2200000083","article-title":"Advances and open problems in federated learning","volume":"14","author":"Kairouz","year":"2021","journal-title":"Found. Trends Mach. Learn."},{"key":"10.1016\/j.future.2023.12.030_b25","series-title":"Fedml: A research library and benchmark for federated machine learning","author":"He","year":"2020"},{"issue":"9","key":"10.1016\/j.future.2023.12.030_b26","doi-asserted-by":"crossref","first-page":"6778","DOI":"10.1109\/JIOT.2021.3112737","article-title":"Nosnoop: An effective collaborative meta-learning scheme against property inference attack","volume":"9","author":"Ma","year":"2022","journal-title":"IEEE Internet Things J."},{"key":"10.1016\/j.future.2023.12.030_b27","series-title":"Proceedings of the 2022 ACM SIGSAC Conference on Computer and Communications Security, CCS","first-page":"3093","article-title":"Enhanced membership inference attacks against machine learning models","author":"Ye","year":"2022"},{"key":"10.1016\/j.future.2023.12.030_b28","series-title":"2018 IEEE Winter Conference on Applications of Computer Vision, WACV","first-page":"839","article-title":"Grad-cam++: Generalized gradient-based visual explanations for deep convolutional networks","author":"Chattopadhyay","year":"2018"},{"key":"10.1016\/j.future.2023.12.030_b29","series-title":"2015 IEEE International Conference on Computer Vision, ICCV","first-page":"3730","article-title":"Deep learning face attributes in the wild","author":"Liu","year":"2015"},{"key":"10.1016\/j.future.2023.12.030_b30","series-title":"2017 International Joint Conference on Neural Networks, IJCNN","first-page":"2921","article-title":"EMNIST: extending MNIST to handwritten letters","author":"Cohen","year":"2017"},{"issue":"2\u20134","key":"10.1016\/j.future.2023.12.030_b31","doi-asserted-by":"crossref","first-page":"144","DOI":"10.1007\/s11263-016-0940-3","article-title":"Deep expectation of real and apparent age from a single image without facial landmarks","volume":"126","author":"Rothe","year":"2018","journal-title":"Int. J. Comput. Vis."},{"issue":"11","key":"10.1016\/j.future.2023.12.030_b32","doi-asserted-by":"crossref","first-page":"2278","DOI":"10.1109\/5.726791","article-title":"Gradient-based learning applied to document recognition","volume":"86","author":"LeCun","year":"1998","journal-title":"Proc. IEEE"},{"key":"10.1016\/j.future.2023.12.030_b33","series-title":"Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA, August 13-17, 2016","first-page":"1135","article-title":"Why should I trust you?: Explaining the predictions of any classifier","author":"Ribeiro","year":"2016"},{"key":"10.1016\/j.future.2023.12.030_b34","series-title":"Smoothgrad: removing noise by adding noise","author":"Smilkov","year":"2017"},{"key":"10.1016\/j.future.2023.12.030_b35","unstructured":"J. MacQueen, Some methods for classification and analysis of multivariate observations, in: Proc. 5th Berkeley Symposium on Math. Stat. and Prob., 1965, p. 281."},{"key":"10.1016\/j.future.2023.12.030_b36","series-title":"2017 IEEE International Conference on Computer Design, ICCD","first-page":"45","article-title":"Neural trojans","author":"Liu","year":"2017"},{"key":"10.1016\/j.future.2023.12.030_b37","series-title":"2019 IEEE Symposium on Security and Privacy, SP 2019, San Francisco, CA, USA, May 19-23, 2019","first-page":"707","article-title":"Neural cleanse: Identifying and mitigating backdoor attacks in neural networks","author":"Wang","year":"2019"},{"key":"10.1016\/j.future.2023.12.030_b38","series-title":"ASIA CCS \u201921: ACM Asia Conference on Computer and Communications Security","first-page":"363","article-title":"Deepsweep: An evaluation framework for mitigating DNN backdoor attacks using data augmentation","author":"Qiu","year":"2021"},{"key":"10.1016\/j.future.2023.12.030_b39","series-title":"The 23rd International Conference on Artificial Intelligence and Statistics, AISTATS 2020 26-28 2020, Online [Palermo, Sicily, Italy]","first-page":"2938","article-title":"How to backdoor federated learning","volume":"vol. 108","author":"Bagdasaryan","year":"2020"},{"key":"10.1016\/j.future.2023.12.030_b40","series-title":"8th International Conference on Learning Representations, ICLR 2020, Addis Ababa, Ethiopia, April 26-30, 2020","article-title":"DBA: distributed backdoor attacks against federated learning","author":"Xie","year":"2020"}],"container-title":["Future Generation Computer Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0167739X23004946?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0167739X23004946?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2024,2,19]],"date-time":"2024-02-19T20:45:21Z","timestamp":1708375521000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0167739X23004946"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,5]]},"references-count":40,"alternative-id":["S0167739X23004946"],"URL":"https:\/\/doi.org\/10.1016\/j.future.2023.12.030","relation":{},"ISSN":["0167-739X"],"issn-type":[{"value":"0167-739X","type":"print"}],"subject":[],"published":{"date-parts":[[2024,5]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"FLPM: A property modification scheme for data protection in federated learning","name":"articletitle","label":"Article Title"},{"value":"Future Generation Computer Systems","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.future.2023.12.030","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2023 Elsevier B.V. All rights reserved.","name":"copyright","label":"Copyright"}]}}