{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,22]],"date-time":"2025-03-22T04:03:21Z","timestamp":1742616201218,"version":"3.40.2"},"publisher-location":"Singapore","reference-count":38,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819637737","type":"print"},{"value":"9789819637744","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025]]},"DOI":"10.1007\/978-981-96-3774-4_9","type":"book-chapter","created":{"date-parts":[[2025,3,21]],"date-time":"2025-03-21T04:51:49Z","timestamp":1742532709000},"page":"138-156","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["FLAIR: A Federated Learning Approach Against Inference Attacks and\u00a0Risks"],"prefix":"10.1007","author":[{"given":"Mouad","family":"Bouharoun","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mohammed","family":"Erradi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,3,21]]},"reference":[{"key":"9_CR1","doi-asserted-by":"crossref","unstructured":"Li, J., Tong, X., Liu, J.,Cheng, L.: An efficient federated learning system for network intrusion detection. IEEE Syst. J. 17(2), 2455\u20132464 (2023)","DOI":"10.1109\/JSYST.2023.3236995"},{"key":"9_CR2","doi-asserted-by":"crossref","unstructured":"Chen, Z., Lv, N., Liu, P., Fang, Y., Chen, K., Pan, W.: Intrusion detection for wireless edge networks based on federated learning. IEEE Access 8 (2020)","DOI":"10.1109\/ACCESS.2020.3041793"},{"key":"9_CR3","doi-asserted-by":"crossref","unstructured":"Verma, P., De Leon, M.P., Breslin, J.G., O\u2019Shea, D.: FedTIU: securing virtualized PLCs against DDoS attacks using a federated learning enabled threat intelligence unit. In: IEEE International Conference on Smart Computing (SMARTCOMP), pp. 233\u2013236 (2023)","DOI":"10.1109\/SMARTCOMP58114.2023.00058"},{"key":"9_CR4","doi-asserted-by":"crossref","unstructured":"Rashid, M.M., Khan, S.U., Eusufzai, F., Redwan, M.A., Sabuj, S.R., Elsharief, M.: A Federated learning-based approach for improving intrusion detection in industrial internet of things networks. Network 3, 158\u2013179 (2023)","DOI":"10.3390\/network3010008"},{"key":"9_CR5","doi-asserted-by":"crossref","unstructured":"Wang, X., Wang, N., Wu, L., Guan, Z., Du, X., Guizani, M.: GBMIA: gradient-based membership inference attack in federated learning. In: IEEE International Conference on Communications (ICC), pp. 5066\u20135071 (2023)","DOI":"10.1109\/ICC45041.2023.10279702"},{"key":"9_CR6","doi-asserted-by":"crossref","unstructured":"Hu, H., Salcic, Z., Sun, L., Dobbie, G., Zhang, X.: Source inference attacks in federated learning. In: IEEE International Conference on Data Mining (ICDM), pp. 1102\u20131107 (2021)","DOI":"10.1109\/ICDM51629.2021.00129"},{"key":"9_CR7","doi-asserted-by":"crossref","unstructured":"Boenisch, F., Dziedzic, A., Schuster, R., Shamsabadi, A.S., Shumailov, I., Papernot, N.: Reconstructing individual data points in federated learning hardened with differential privacy and secure aggregation. In: IEEE European Symposium on Security and Privacy (EuroS &P), pp. 241\u2013257 (2023)","DOI":"10.1109\/EuroSP57164.2023.00023"},{"key":"9_CR8","doi-asserted-by":"crossref","unstructured":"Luo, X., Wu, Y., Xiao, X., Ooi, B.C.: Feature inference attack on model predictions in vertical federated learning. In: IEEE International Conference on Data Engineering (ICDE), pp. 181\u2013192 (2021)","DOI":"10.1109\/ICDE51399.2021.00023"},{"issue":"2\/3","key":"9_CR9","first-page":"117","volume":"18","author":"T Ha","year":"2022","unstructured":"Ha, T., Dang, T.K.: Inference attacks based on GAN in federated learning. Int. J. Web Inf. Syst. 18(2\/3), 117\u2013136 (2022)","journal-title":"Int. J. Web Inf. Syst."},{"key":"9_CR10","doi-asserted-by":"crossref","unstructured":"Gu, Y., Bai, Y., Xu, S.: CS-MIA: membership inference attack based on prediction confidence series in federated learning. J. Inf. Secur. Appl. 67, 103201 (2022)","DOI":"10.1016\/j.jisa.2022.103201"},{"key":"9_CR11","doi-asserted-by":"crossref","unstructured":"Hatamizadeh, A., et al.: Do gradient inversion attacks make federated learning unsafe?. IEEE Trans. Med. Imaging (2023)","DOI":"10.1109\/TMI.2023.3239391"},{"key":"9_CR12","doi-asserted-by":"crossref","unstructured":"Hu, H., Zhang, X., Salcic, Z., Sun, L., Choo, K.K.R., Dobbie, G.: Source inference attacks: beyond membership inference attacks in federated learning. IEEE Trans. Depend. Secure Comput. (2023)","DOI":"10.1109\/TDSC.2023.3321565"},{"key":"9_CR13","doi-asserted-by":"crossref","unstructured":"Chen, Y., Gui, Y., Lin, H., Gan, W., Wu, Y.: Federated learning attacks and defenses: a survey. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 4256\u20134265 (2022)","DOI":"10.1109\/BigData55660.2022.10020431"},{"key":"9_CR14","doi-asserted-by":"crossref","unstructured":"Hohman, F., Kery, M.B., Ren, D., Moritz, D.: Model compression in practice: lessons learned from practitioners creating on-device machine learning experiences. arXiv preprint arXiv:2310.04621 (2023)","DOI":"10.1145\/3613904.3642109"},{"key":"9_CR15","unstructured":"de Vos, M., Dhasade, A., Kermarrec, A.-M., Lavoie, E., Pouwelse, J.A.: MoDeST: bridging the gap between federated and decentralized learning with decentralized sampling. arXiv preprint arXiv:2302.13837 (2023)"},{"key":"9_CR16","doi-asserted-by":"crossref","unstructured":"Wang, P., Sun, W., Zhang, H., Ma, W., Zhang, Y.: Distributed and secure federated learning for wireless computing power networks. IEEE Trans. Veh. Technol. 72(7), 9381\u20139393 (2023)","DOI":"10.1109\/TVT.2023.3247859"},{"key":"9_CR17","unstructured":"McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. Artif. Intell. Stat. 1273\u20131282 (2017)"},{"key":"9_CR18","doi-asserted-by":"publisher","first-page":"619","DOI":"10.1016\/j.future.2020.10.007","volume":"115","author":"V Mothukuri","year":"2021","unstructured":"Mothukuri, V., Parizi, R.M., Pouriyeh, S., Huang, Y., Dehghantanha, A., Srivastava, G.: A survey on security and privacy of federated learning. Futur. Gener. Comput. Syst. 115, 619\u2013640 (2021)","journal-title":"Futur. Gener. Comput. Syst."},{"key":"9_CR19","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2021.104468","volume":"106","author":"A Blanco-Justicia","year":"2021","unstructured":"Blanco-Justicia, A., Domingo-Ferrer, J., Mart\u00ednez, S., S\u00e1nchez, D., Flanagan, A., Tan, K.E.: Achieving security and privacy in federated learning systems: survey, research challenges and future directions. Eng. Appl. Artif. Intell. 106, 104468 (2021)","journal-title":"Eng. Appl. Artif. Intell."},{"key":"9_CR20","doi-asserted-by":"crossref","unstructured":"Tolpegin, V., Truex, S., Gursoy, M.E., Liu, L.: Data poisoning attacks against federated learning systems. In: European Symposium on Research in Computer Security, pp. 480\u2013501 (2020)","DOI":"10.1007\/978-3-030-58951-6_24"},{"key":"9_CR21","unstructured":"Hamilton, W.L., Ying, R., Leskovec, J.: Representation learning on graphs: methods and applications. arXiv preprint arXiv:1709.05584 (2017)"},{"key":"9_CR22","unstructured":"Herzog, R., K\u00f6hne, F., Kreis, L., Schiela, A.: Frobenius-type norms and inner products of matrices and linear maps with applications to neural network training. arXiv preprint arXiv:2311.15419 (2023)"},{"key":"9_CR23","first-page":"7232","volume":"34","author":"Y Huang","year":"2021","unstructured":"Huang, Y., Gupta, S., Song, Z., Li, K., Arora, S.: Evaluating gradient inversion attacks and defenses in federated learning. Adv. Neural. Inf. Process. Syst. 34, 7232\u20137241 (2021)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"9_CR24","unstructured":"Suri, A., Kanani, P., Marathe, V.J., Peterson, D.W.: Subject membership inference attacks in federated learning. arXiv preprint arXiv:2206.03317 (2022)"},{"key":"9_CR25","unstructured":"Zari, O., Xu, C., Neglia, G.: Efficient passive membership inference attack in federated learning. arXiv preprint arXiv:2111.00430 (2021)"},{"key":"9_CR26","doi-asserted-by":"crossref","unstructured":"Sarhan, M., Layeghy, S., Moustafa, N., Portmann, M.: NetFlow datasets for machine learning-based network intrusion detection systems. In: Big Data Technologies and Applications (2020)","DOI":"10.1007\/978-3-030-72802-1_9"},{"key":"9_CR27","doi-asserted-by":"publisher","first-page":"22359","DOI":"10.1109\/ACCESS.2022.3151670","volume":"10","author":"A El Ouadrhiri","year":"2022","unstructured":"El Ouadrhiri, A., Abdelhadi, A.: Differential privacy for deep and federated learning: a survey. IEEE Access 10, 22359\u201322380 (2022)","journal-title":"IEEE Access"},{"key":"9_CR28","doi-asserted-by":"crossref","unstructured":"Xu, M., Li, X.: Subject property inference attack in collaborative learning. In: International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC), vol. 1 (2020)","DOI":"10.1109\/IHMSC49165.2020.00057"},{"key":"9_CR29","unstructured":"Suri, A., Evans, D.: Formalizing and estimating distribution inference risks. arXiv preprint arXiv:2109.06024 (2021)"},{"key":"9_CR30","doi-asserted-by":"crossref","unstructured":"Wei, K., et al.: Federated learning with differential privacy : algorithms and performance analysis. IEEE Trans. Inf. Forensics Secur. 15, 3454\u20133469 (2020)","DOI":"10.1109\/TIFS.2020.2988575"},{"key":"9_CR31","doi-asserted-by":"crossref","unstructured":"Ganju, K., Wang, Q., Yang, W., Gunter, C.A., Borisov, N.: Property inference attacks on fully connected neural networks using permutation invariant representations. In: Conference on Computer and Communications Security (CCS) (2018)","DOI":"10.1145\/3243734.3243834"},{"key":"9_CR32","unstructured":"Jin, W., et al.: FedML-HE: an efficient homomorphic-encryption-based privacy-preserving federated learning system. arXiv preprint arXiv:2303.10837 (2023)"},{"issue":"9","key":"9_CR33","doi-asserted-by":"publisher","first-page":"5880","DOI":"10.1002\/int.22818","volume":"37","author":"J Ma","year":"2022","unstructured":"Ma, J., Naas, S.A., Sigg, S., Lyu, X.: Privacy-preserving federated learning based on multi-key homomorphic encryption. Int. J. Intell. Syst. 37(9), 5880\u20135901 (2022)","journal-title":"Int. J. Intell. Syst."},{"issue":"7","key":"9_CR34","doi-asserted-by":"publisher","first-page":"1235","DOI":"10.1162\/neco_a_01199","volume":"31","author":"Y Yu","year":"2019","unstructured":"Yu, Y., Si, X., Hu, C., Zhang, J.: A review of recurrent neural networks: LSTM cells and network architectures. Neural Comput. 31(7), 1235\u20131270 (2019)","journal-title":"Neural Comput."},{"key":"9_CR35","unstructured":"Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 (2016)"},{"key":"9_CR36","doi-asserted-by":"crossref","unstructured":"Steinmetz, R., Wehrle, K.: Peer-to-Peer Systems and Applications, vol. 3485 (2005)","DOI":"10.1007\/11530657"},{"issue":"2","key":"9_CR37","doi-asserted-by":"publisher","first-page":"139","DOI":"10.1109\/TC.2003.1176982","volume":"52","author":"AJ Ganesh","year":"2003","unstructured":"Ganesh, A.J., Kermarrec, A.M., Massouli\u00e9, L.: Peer-to-peer membership management for gossip-based protocols. IEEE Trans. Comput. 52(2), 139\u2013149 (2003)","journal-title":"IEEE Trans. Comput."},{"issue":"5","key":"9_CR38","doi-asserted-by":"publisher","first-page":"60","DOI":"10.1109\/MC.2004.1297243","volume":"37","author":"PT Eugster","year":"2004","unstructured":"Eugster, P.T., Guerraoui, R., Kermarrec, A.M., Massouli\u00e9, L.: Epidemic information dissemination in distributed systems. Computer 37(5), 60\u201367 (2004)","journal-title":"Computer"}],"container-title":["Lecture Notes in Computer Science","Security and Privacy in Social Networks and Big Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-96-3774-4_9","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,3,21]],"date-time":"2025-03-21T04:52:13Z","timestamp":1742532733000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-96-3774-4_9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9789819637737","9789819637744"],"references-count":38,"URL":"https:\/\/doi.org\/10.1007\/978-981-96-3774-4_9","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"21 March 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"SocialSec","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Symposium on Security and Privacy in Social Networks and Big Data","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Abu Dhabi","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"United Arab Emirates","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19 November 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21 November 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"10","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"socialsec2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}