{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,19]],"date-time":"2026-02-19T07:22:07Z","timestamp":1771485727938,"version":"3.50.1"},"reference-count":55,"publisher":"Springer Science and Business Media LLC","issue":"13","license":[{"start":{"date-parts":[[2024,3,1]],"date-time":"2024-03-01T00:00:00Z","timestamp":1709251200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,3,1]],"date-time":"2024-03-01T00:00:00Z","timestamp":1709251200000},"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":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2024,5]]},"DOI":"10.1007\/s00521-024-09464-w","type":"journal-article","created":{"date-parts":[[2024,3,1]],"date-time":"2024-03-01T13:02:53Z","timestamp":1709298173000},"page":"7359-7372","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["EVAD: encrypted vibrational anomaly detection with homomorphic encryption"],"prefix":"10.1007","volume":"36","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0460-1690","authenticated-orcid":false,"given":"Alessandro","family":"Falcetta","sequence":"first","affiliation":[]},{"given":"Manuel","family":"Roveri","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,3,1]]},"reference":[{"issue":"4","key":"9464_CR1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3214303","volume":"51","author":"A Acar","year":"2018","unstructured":"Acar A, Aksu H, Uluagac AS et al (2018) A survey on homomorphic encryption schemes: theory and implementation. ACM Comput Surv (Csur) 51(4):1\u201335","journal-title":"ACM Comput Surv (Csur)"},{"key":"9464_CR2","doi-asserted-by":"publisher","first-page":"278","DOI":"10.1016\/j.future.2015.01.001","volume":"55","author":"M Ahmed","year":"2016","unstructured":"Ahmed M, Mahmood AN, Islam MR (2016) A survey of anomaly detection techniques in financial domain. Futur Gener Comput Syst 55:278\u2013288","journal-title":"Futur Gener Comput Syst"},{"issue":"18","key":"9464_CR3","doi-asserted-by":"publisher","first-page":"15555","DOI":"10.1007\/s00521-022-07202-8","volume":"34","author":"A Al Badawi","year":"2022","unstructured":"Al Badawi A, Chen L, Vig S (2022) Fast homomorphic SVM inference on encrypted data. Neural Comput Appl 34(18):15555\u201315573","journal-title":"Neural Comput Appl"},{"key":"9464_CR4","doi-asserted-by":"publisher","first-page":"28","DOI":"10.1016\/j.jcss.2017.03.001","volume":"90","author":"A Alabdulatif","year":"2017","unstructured":"Alabdulatif A, Kumarage H, Khalil I et al (2017) Privacy-preserving anomaly detection in cloud with lightweight homomorphic encryption. J Comput Syst Sci 90:28\u201345","journal-title":"J Comput Syst Sci"},{"key":"9464_CR5","doi-asserted-by":"publisher","first-page":"209","DOI":"10.1016\/j.jpdc.2017.12.011","volume":"127","author":"A Alabdulatif","year":"2019","unstructured":"Alabdulatif A, Khalil I, Kumarage H et al (2019) Privacy-preserving anomaly detection in the cloud for quality assured decision-making in smart cities. J Parallel Distrib Comput 127:209\u2013223","journal-title":"J Parallel Distrib Comput"},{"issue":"9","key":"9464_CR6","doi-asserted-by":"publisher","first-page":"6326","DOI":"10.1109\/TII.2022.3164741","volume":"18","author":"M Alazab","year":"2022","unstructured":"Alazab M, Gadekallu TR, Su C (2022) Guest editorial: security and privacy issues in industry 4.0 applications. IEEE Trans Ind Inform 18(9):6326\u20136329","journal-title":"IEEE Trans Ind Inform"},{"key":"9464_CR7","doi-asserted-by":"crossref","unstructured":"Alexandru AB, Burbano L, \u00c7eliktu\u011f MF et\u00a0al (2022) Private anomaly detection in linear controllers: Garbled circuits vs. homomorphic encryption. In: 2022 IEEE 61st conference on decision and control (CDC). IEEE, pp 7746\u20137753","DOI":"10.1109\/CDC51059.2022.9992616"},{"key":"9464_CR8","unstructured":"Benaissa A, Retiat B, Cebere B et\u00a0al (2021) Tenseal: a library for encrypted tensor operations using homomorphic encryption. arxiv:2104.03152"},{"issue":"9","key":"9464_CR9","first-page":"15","volume":"105","author":"P Bholowalia","year":"2014","unstructured":"Bholowalia P, Kumar A (2014) Ebk-means: a clustering technique based on elbow method and k-means in WSN. Int J Comput Appl 105(9):15","journal-title":"Int J Comput Appl"},{"key":"9464_CR10","doi-asserted-by":"crossref","unstructured":"Boemer F, Costache A, Cammarota R et\u00a0al (2019) nGraph-HE2: a high-throughput framework for neural network inference on encrypted data. In: Proceedings of the 7th ACM workshop on encrypted computing & applied homomorphic cryptography, pp 45\u201356","DOI":"10.1145\/3338469.3358944"},{"key":"9464_CR11","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1016\/j.neucom.2019.11.041","volume":"384","author":"A Boulemtafes","year":"2020","unstructured":"Boulemtafes A, Derhab A, Challal Y (2020) A review of privacy-preserving techniques for deep learning. Neurocomputing 384:21\u201345. https:\/\/doi.org\/10.1016\/j.neucom.2019.11.041","journal-title":"Neurocomputing"},{"key":"9464_CR12","unstructured":"Campbell C, Bennett K (2000) A linear programming approach to novelty detection. In: Advances in neural information processing systems, Vol 13, MIT Press, https:\/\/proceedings.neurips.cc\/paper_files\/paper\/2000\/file\/0e087ec55dcbe7b2d7992d6b69b519fb-Paper.pdf"},{"issue":"8","key":"9464_CR13","doi-asserted-by":"publisher","first-page":"95","DOI":"10.1109\/MC.2022.3178169","volume":"55","author":"M Campbell","year":"2022","unstructured":"Campbell M (2022) Privacy-preserving computation: doomed to succeed. Computer 55(8):95\u201399","journal-title":"Computer"},{"key":"9464_CR14","doi-asserted-by":"crossref","unstructured":"Chen T, Bao H, Huang S et\u00a0al (2022) The-x: privacy-preserving transformer inference with homomorphic encryption. arXiv preprint arXiv:2206.00216","DOI":"10.18653\/v1\/2022.findings-acl.277"},{"key":"9464_CR15","doi-asserted-by":"crossref","unstructured":"Cheon JH, Kim A, Kim M et\u00a0al (2017) Homomorphic encryption for arithmetic of approximate numbers. In: International conference on the theory and application of cryptology and information security. Springer, pp 409\u2013437","DOI":"10.1007\/978-3-319-70694-8_15"},{"key":"9464_CR16","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9780511801389","volume-title":"Support vector machines and other kernel-based learning methods","author":"N Christianini","year":"2000","unstructured":"Christianini N, Shawe-Taylor J (2000) Support vector machines and other kernel-based learning methods. Cambridge University Press, Cambridge"},{"issue":"10","key":"9464_CR17","first-page":"8216","volume":"69","author":"AL Ellefsen","year":"2020","unstructured":"Ellefsen AL, Han P, Cheng X et al (2020) Online fault detection in autonomous ferries: using fault-type independent spectral anomaly detection. IEEE Trans Instrum Meas 69(10):8216\u20138225","journal-title":"IEEE Trans Instrum Meas"},{"issue":"3","key":"9464_CR18","doi-asserted-by":"publisher","first-page":"14","DOI":"10.1109\/MCI.2022.3180883","volume":"17","author":"A Falcetta","year":"2022","unstructured":"Falcetta A, Roveri M (2022) Privacy-preserving deep learning with homomorphic encryption: an introduction. IEEE Comput Intell Mag 17(3):14\u201325","journal-title":"IEEE Comput Intell Mag"},{"key":"9464_CR19","doi-asserted-by":"crossref","unstructured":"Falcetta A, Roveri M (2022b) Privacy-preserving time series prediction with temporal convolutional neural networks. In: 2022 international joint conference on neural networks (IJCNN), IEEE, pp 1\u20138","DOI":"10.1109\/IJCNN55064.2022.9892823"},{"key":"9464_CR20","doi-asserted-by":"crossref","unstructured":"Falcetta A, Pavan M, Canali S et\u00a0al (2023) To personalize or not to personalize? Soft personalization and the ethics of ML for health. In: 2023 IEEE 10th international conference on data science and advanced analytics (DSAA). IEEE, pp 1\u201310","DOI":"10.1109\/DSAA60987.2023.10302472"},{"issue":"7","key":"9464_CR21","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3464423","volume":"54","author":"T Fernando","year":"2021","unstructured":"Fernando T, Gammulle H, Denman S et al (2021) Deep learning for medical anomaly detection\u2014a survey. ACM Comput Surv (CSUR) 54(7):1\u201337","journal-title":"ACM Comput Surv (CSUR)"},{"issue":"12","key":"9464_CR22","doi-asserted-by":"publisher","first-page":"1901","DOI":"10.1109\/PROC.1966.5273","volume":"54","author":"MJ Flynn","year":"1966","unstructured":"Flynn MJ (1966) Very high-speed computing systems. Proc IEEE 54(12):1901\u20131909","journal-title":"Proc IEEE"},{"key":"9464_CR23","unstructured":"Gilad-Bachrach R, Dowlin N, Laine K et\u00a0al (2016) Cryptonets: applying neural networks to encrypted data with high throughput and accuracy. In: International conference on machine learning, PMLR, pp 201\u2013210"},{"issue":"2","key":"9464_CR24","doi-asserted-by":"publisher","first-page":"270","DOI":"10.1016\/0022-0000(84)90070-9","volume":"28","author":"S Goldwasser","year":"1984","unstructured":"Goldwasser S, Micali S (1984) Probabilistic encryption. J Comput Syst Sci 28(2):270\u2013299","journal-title":"J Comput Syst Sci"},{"issue":"5","key":"9464_CR25","doi-asserted-by":"publisher","first-page":"72","DOI":"10.1145\/3572832","volume":"66","author":"S Gorantala","year":"2023","unstructured":"Gorantala S, Springer R, Gipson B (2023) Unlocking the potential of fully homomorphic encryption. Commun ACM 66(5):72\u201381","journal-title":"Commun ACM"},{"key":"9464_CR26","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S et\u00a0al (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"key":"9464_CR27","unstructured":"Hijmans H, Raab CD (2018) Ethical dimensions of the GDPR. Edward Elgar, Commentary on the General Data Protection Regulation, Cheltenham (Forthcoming))"},{"issue":"6","key":"9464_CR28","doi-asserted-by":"publisher","first-page":"5938","DOI":"10.1007\/s10489-021-02727-2","volume":"52","author":"H Huang","year":"2022","unstructured":"Huang H, Wang Y, Zong H (2022) Support vector machine classification over encrypted data. Appl Intell 52(6):5938\u20135948","journal-title":"Appl Intell"},{"key":"9464_CR29","doi-asserted-by":"crossref","unstructured":"Iliadis L, Pimenidis E (2023) Technologies of the 4th industrial revolution with applications. Neural Comput Appl 35:21331\u201321332","DOI":"10.1007\/s00521-023-08986-z"},{"key":"9464_CR30","doi-asserted-by":"publisher","first-page":"143608","DOI":"10.1109\/ACCESS.2019.2944689","volume":"7","author":"W Jiang","year":"2019","unstructured":"Jiang W, Hong Y, Zhou B et al (2019) A GAN-based anomaly detection approach for imbalanced industrial time series. IEEE Access 7:143608\u2013143619","journal-title":"IEEE Access"},{"issue":"1","key":"9464_CR31","doi-asserted-by":"publisher","first-page":"44","DOI":"10.3390\/jcp3010004","volume":"3","author":"R Kiesel","year":"2023","unstructured":"Kiesel R, Lakatsch M, Mann A et al (2023) Potential of homomorphic encryption for cloud computing use cases in manufacturing. J Cybersecur Privacy 3(1):44\u201360. https:\/\/doi.org\/10.3390\/jcp3010004","journal-title":"J Cybersecur Privacy"},{"key":"9464_CR32","doi-asserted-by":"publisher","DOI":"10.1016\/j.dib.2022.108315","volume":"42","author":"D Kumar","year":"2022","unstructured":"Kumar D, Mehran S, Shaikh MZ et al (2022) Triaxial bearing vibration dataset of induction motor under varying load conditions. Data Brief 42:108315","journal-title":"Data Brief"},{"issue":"12","key":"9464_CR33","doi-asserted-by":"publisher","first-page":"2182","DOI":"10.1093\/jamia\/ocac165","volume":"29","author":"TT Kuo","year":"2022","unstructured":"Kuo TT, Jiang X, Tang H et al (2022) The evolving privacy and security concerns for genomic data analysis and sharing as observed from the iDASH competition. J Am Med Inform Assoc 29(12):2182\u20132190","journal-title":"J Am Med Inform Assoc"},{"issue":"4","key":"9464_CR34","doi-asserted-by":"publisher","first-page":"541","DOI":"10.1162\/neco.1989.1.4.541","volume":"1","author":"Y LeCun","year":"1989","unstructured":"LeCun Y, Boser B, Denker JS et al (1989) Backpropagation applied to handwritten zip code recognition. Neural Comput 1(4):541\u2013551","journal-title":"Neural Comput"},{"key":"9464_CR35","unstructured":"Lee E, Lee JW, Lee J et\u00a0al (2022) Low-complexity deep convolutional neural networks on fully homomorphic encryption using multiplexed parallel convolutions. In: International conference on machine learning, PMLR, pp 12403\u201312422"},{"key":"9464_CR36","unstructured":"Loparo K (2012) Case western reserve university bearing data center. Bearings Vibration Data Sets, Case Western Reserve University pp 22\u201328"},{"key":"9464_CR37","doi-asserted-by":"crossref","unstructured":"Lyubashevsky V, Peikert C, Regev O (2010) On ideal lattices and learning with errors over rings. In: Annual international conference on the theory and applications of cryptographic techniques. Springer, pp 1\u201323","DOI":"10.1007\/978-3-642-13190-5_1"},{"key":"9464_CR38","doi-asserted-by":"crossref","unstructured":"Lyubashevsky V, Peikert C, Regev O (2013) A toolkit for ring-LWE cryptography. In: Advances in cryptology\u2014EUROCRYPT 2013: 32nd annual international conference on the theory and applications of cryptographic techniques, Athens, Greece, May 26\u201330, 2013. Proceedings 32. Springer, pp 35\u201354","DOI":"10.1007\/978-3-642-38348-9_3"},{"key":"9464_CR39","first-page":"106","volume":"21","author":"K Manheim","year":"2019","unstructured":"Manheim K, Kaplan L (2019) Artificial intelligence: risks to privacy and democracy. Yale J Law Technol 21:106","journal-title":"Yale J Law Technol"},{"key":"9464_CR40","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2021.106816","volume":"216","author":"G Michau","year":"2021","unstructured":"Michau G, Fink O (2021) Unsupervised transfer learning for anomaly detection: application to complementary operating condition transfer. Knowl Based Syst 216:106816","journal-title":"Knowl Based Syst"},{"issue":"2","key":"9464_CR41","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3439950","volume":"54","author":"G Pang","year":"2021","unstructured":"Pang G, Shen C, Cao L et al (2021) Deep learning for anomaly detection: a review. ACM Comput Surv (CSUR) 54(2):1\u201338","journal-title":"ACM Comput Surv (CSUR)"},{"key":"9464_CR42","doi-asserted-by":"crossref","unstructured":"Park S, Byun J, Lee J (2022) Privacy-preserving fair learning of support vector machine with homomorphic encryption. In: Proceedings of the ACM web conference, 2022, pp 3572\u20133583","DOI":"10.1145\/3485447.3512252"},{"key":"9464_CR43","first-page":"2825","volume":"12","author":"F Pedregosa","year":"2011","unstructured":"Pedregosa F, Varoquaux G, Gramfort A et al (2011) Scikit-learn: machine learning in Python. J Mach Learn Res 12:2825\u20132830","journal-title":"J Mach Learn Res"},{"key":"9464_CR44","doi-asserted-by":"publisher","first-page":"298","DOI":"10.1016\/j.procs.2021.08.031","volume":"192","author":"PJ Pereira","year":"2021","unstructured":"Pereira PJ, Coelho G, Ribeiro A et al (2021) Using deep autoencoders for in-vehicle audio anomaly detection. Procedia Comput Sci 192:298\u2013307","journal-title":"Procedia Comput Sci"},{"key":"9464_CR45","doi-asserted-by":"publisher","first-page":"664","DOI":"10.1016\/j.neucom.2017.06.053","volume":"267","author":"A Saxena","year":"2017","unstructured":"Saxena A, Prasad M, Gupta A et al (2017) A review of clustering techniques and developments. Neurocomputing 267:664\u2013681","journal-title":"Neurocomputing"},{"issue":"7","key":"9464_CR46","doi-asserted-by":"publisher","first-page":"1443","DOI":"10.1162\/089976601750264965","volume":"13","author":"B Sch\u00f6lkopf","year":"2001","unstructured":"Sch\u00f6lkopf B, Platt JC, Shawe-Taylor J et al (2001) Estimating the support of a high-dimensional distribution. Neural Comput 13(7):1443\u20131471","journal-title":"Neural Comput"},{"key":"9464_CR47","unstructured":"SEAL (2023) Microsoft SEAL (release 4.1). https:\/\/github.com\/Microsoft\/SEAL, microsoft Research, Redmond, WA"},{"key":"9464_CR48","volume":"28","author":"PM Seeger","year":"2022","unstructured":"Seeger PM, Yahouni Z, Alpan G (2022) Literature review on using data mining in production planning and scheduling within the context of cyber physical systems. J Ind Inf Integr 28:100371","journal-title":"J Ind Inf Integr"},{"key":"9464_CR49","doi-asserted-by":"crossref","unstructured":"Sgaglione L, Coppolino L, D\u2019Antonio S et\u00a0al (2019) Privacy preserving intrusion detection via homomorphic encryption. In: 2019 IEEE 28th international conference on enabling technologies: infrastructure for collaborative enterprises (WETICE). IEEE, pp 321\u2013326","DOI":"10.1109\/WETICE.2019.00073"},{"key":"9464_CR50","doi-asserted-by":"publisher","first-page":"57","DOI":"10.1007\/s10623-012-9720-4","volume":"71","author":"NP Smart","year":"2014","unstructured":"Smart NP, Vercauteren F (2014) Fully homomorphic SIMD operations. Des Codes Crypt 71:57\u201381","journal-title":"Des Codes Crypt"},{"key":"9464_CR51","doi-asserted-by":"crossref","unstructured":"Trivedi D, Boudguiga A, Triandopoulos N (2023) Sigml: supervised log anomaly with fully homomorphic encryption. In: International symposium on cyber security, cryptology, and machine learning. Springer, pp 372\u2013388","DOI":"10.1007\/978-3-031-34671-2_26"},{"key":"9464_CR52","doi-asserted-by":"crossref","unstructured":"Ulybyshev D, Bare C, Bellisario K et\u00a0al (2020) Protecting electronic health records in transit and at rest. In: 2020 IEEE 33rd International symposium on computer-based medical systems (CBMS). IEEE, pp 449\u2013452","DOI":"10.1109\/CBMS49503.2020.00091"},{"issue":"18","key":"9464_CR53","doi-asserted-by":"publisher","first-page":"15661","DOI":"10.1007\/s00521-022-07225-1","volume":"34","author":"J Yuan","year":"2022","unstructured":"Yuan J, Cao S, Ren G et al (2022) LW-Net: an interpretable network with smart lifting wavelet kernel for mechanical feature extraction and fault diagnosis. Neural Comput Appl 34(18):15661\u201315672","journal-title":"Neural Comput Appl"},{"key":"9464_CR54","doi-asserted-by":"crossref","unstructured":"Zheng P, Cai Z, Zeng H et\u00a0al (2022) Keyword spotting in the homomorphic encrypted domain using deep complex-valued CNN. In: Proceedings of the 30th ACM international conference on multimedia, pp 1474\u20131483","DOI":"10.1145\/3503161.3548350"},{"key":"9464_CR55","doi-asserted-by":"publisher","first-page":"106889","DOI":"10.1016\/j.cie.2020.106889","volume":"150","author":"T Zonta","year":"2020","unstructured":"Zonta T, Da Costa CA, da Rosa Righi R et al (2020) Predictive maintenance in the industry 4.0: a systematic literature review. Comput Ind Eng 150:106889","journal-title":"Comput Ind Eng"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-024-09464-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-024-09464-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-024-09464-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,21]],"date-time":"2024-03-21T13:22:34Z","timestamp":1711027354000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-024-09464-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,3,1]]},"references-count":55,"journal-issue":{"issue":"13","published-print":{"date-parts":[[2024,5]]}},"alternative-id":["9464"],"URL":"https:\/\/doi.org\/10.1007\/s00521-024-09464-w","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,3,1]]},"assertion":[{"value":"10 June 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 January 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 March 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Decalarations"}},{"value":"The authors have no competing interests to declare that are relevant to the content of this article.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}