{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,10]],"date-time":"2025-12-10T16:03:36Z","timestamp":1765382616166,"version":"3.40.5"},"publisher-location":"Cham","reference-count":36,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031897030","type":"print"},{"value":"9783031897047","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\/978-3-031-89704-7_7","type":"book-chapter","created":{"date-parts":[[2025,5,14]],"date-time":"2025-05-14T11:28:20Z","timestamp":1747222100000},"page":"81-95","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Private, Efficient and\u00a0Scalable Kernel Learning for\u00a0Medical Image Analysis"],"prefix":"10.1007","author":[{"given":"Anika","family":"Hannemann","sequence":"first","affiliation":[]},{"given":"Arjhun","family":"Swaminathan","sequence":"additional","affiliation":[]},{"given":"Ali Burak","family":"\u00dcnal","sequence":"additional","affiliation":[]},{"given":"Mete","family":"Akg\u00fcn","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,5,15]]},"reference":[{"issue":"1","key":"7_CR1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-022-05539-7","volume":"12","author":"M Adnan","year":"2022","unstructured":"Adnan, M., Kalra, S., Cresswell, J.C., Taylor, G.W., Tizhoosh, H.R.: Federated learning and differential privacy for medical image analysis. Sci. Rep. 12(1), 1\u201310 (2022)","journal-title":"Sci. Rep."},{"key":"7_CR2","doi-asserted-by":"crossref","unstructured":"Al-Jaroodi, J., Mohamed, N., Abukhousa, E.: Health 4.0: on the way to realizing the healthcare of the future. IEEE Access 8, 211189\u2013211210 (2020)","DOI":"10.1109\/ACCESS.2020.3038858"},{"key":"7_CR3","doi-asserted-by":"crossref","unstructured":"Applebaum, B., Ishai, Y., Kushilevitz, E.: Computationally private randomizing polynomials and their applications. Comput. Complex. 15(2), 115\u2013162 (2006)","DOI":"10.1007\/s00037-006-0211-8"},{"key":"7_CR4","unstructured":"Bagdasaryan, E., Poursaeed, O., Shmatikov, V.: Differential privacy has disparate impact on model accuracy. In: Advances in Neural Information Processing Systems, vol. 32 (2019)"},{"key":"7_CR5","doi-asserted-by":"crossref","unstructured":"Bonawitz, K., et al.: Practical secure aggregation for privacy-preserving machine learning. In: proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security, pp. 1175\u20131191 (2017)","DOI":"10.1145\/3133956.3133982"},{"key":"7_CR6","doi-asserted-by":"crossref","unstructured":"Boser, B.E., Guyon, I.M., Vapnik, V.N.: A training algorithm for optimal margin classifiers. In: Proceedings of the Fifth Annual Workshop on Computational Learning Theory, pp. 144\u2013152 (1992)","DOI":"10.1145\/130385.130401"},{"key":"7_CR7","doi-asserted-by":"crossref","unstructured":"Chen, H., \u00dcnal, A.B., Akg\u00fcn, M., Pfeifer, N.: Privacy-preserving svm on outsourced genomic data via secure multi-party computation. In: Proceedings of the Sixth International Workshop on Security and Privacy Analytics, pp. 61\u201369 (2020)","DOI":"10.1145\/3375708.3380316"},{"key":"7_CR8","doi-asserted-by":"publisher","first-page":"657","DOI":"10.1007\/s10115-010-0362-4","volume":"29","author":"K Chen","year":"2011","unstructured":"Chen, K., Liu, L.: Geometric data perturbation for privacy preserving outsourced data mining. Knowl. Inf. Syst. 29, 657\u2013695 (2011)","journal-title":"Knowl. Inf. Syst."},{"key":"7_CR9","doi-asserted-by":"crossref","unstructured":"Chen, K., Sun, G., Liu, L.: Towards attack-resilient geometric data perturbation. In: proceedings of the 2007 SIAM International Conference on Data Mining, pp. 78\u201389. SIAM (2007)","DOI":"10.1137\/1.9781611972771.8"},{"key":"7_CR10","doi-asserted-by":"crossref","unstructured":"Christensen, O., et\u00a0al.: An introduction to frames and Riesz bases, vol.\u00a07. Springer (2003)","DOI":"10.1007\/978-0-8176-8224-8"},{"key":"7_CR11","doi-asserted-by":"publisher","first-page":"273","DOI":"10.1007\/BF00994018","volume":"20","author":"C Cortes","year":"1995","unstructured":"Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20, 273\u2013297 (1995)","journal-title":"Mach. Learn."},{"key":"7_CR12","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"325","DOI":"10.1007\/978-3-319-29485-8_19","volume-title":"Topics in Cryptology \u2013 CT-RSA 2016","author":"A Costache","year":"2016","unstructured":"Costache, A., Smart, N.P.: Which ring based somewhat homomorphic encryption scheme is best? In: Sako, K. (ed.) CT-RSA 2016. LNCS, vol. 9610, pp. 325\u2013340. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-29485-8_19"},{"issue":"13","key":"7_CR13","doi-asserted-by":"publisher","first-page":"8736","DOI":"10.1021\/acs.chemrev.3c00189","volume":"123","author":"B Dou","year":"2023","unstructured":"Dou, B., et al.: Machine learning methods for small data challenges in molecular science. Chem. Rev. 123(13), 8736\u20138780 (2023)","journal-title":"Chem. Rev."},{"issue":"12","key":"7_CR14","doi-asserted-by":"publisher","first-page":"e28071","DOI":"10.1371\/journal.pone.0028071","volume":"6","author":"K El Emam","year":"2011","unstructured":"El Emam, K., Jonker, E., Arbuckle, L., Malin, B.: A systematic review of re-identification attacks on health data. PLoS ONE 6(12), e28071 (2011)","journal-title":"PLoS ONE"},{"key":"7_CR15","doi-asserted-by":"crossref","unstructured":"Gentry, C.: Fully homomorphic encryption using ideal lattices. In: Proceedings of the Forty-First Annual ACM sySposium on Theory of Computing, pp. 169\u2013178 (2009)","DOI":"10.1145\/1536414.1536440"},{"key":"7_CR16","doi-asserted-by":"crossref","unstructured":"Gower, J.C., Dijksterhuis, G.B.: Procrustes problems, vol.\u00a030. OUP Oxford (2004)","DOI":"10.1093\/acprof:oso\/9780198510581.001.0001"},{"issue":"1","key":"7_CR17","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12911-015-0145-7","volume":"15","author":"L Griebel","year":"2015","unstructured":"Griebel, L., et al.: A scoping review of cloud computing in healthcare. BMC Med. Inform. Decis. Mak. 15(1), 1\u201316 (2015)","journal-title":"BMC Med. Inform. Decis. Mak."},{"key":"7_CR18","unstructured":"Guha, N., Talwalkar, A., Smith, V.: One-shot federated learning. arXiv preprint arXiv:1902.11175 (2019)"},{"key":"7_CR19","doi-asserted-by":"crossref","unstructured":"Hannemann, A., Friedl, B., Buchmann, E.: Differentially private multi-label learning is harder than you\u2019d think. In: 2024 IEEE European Symposium on Security and Privacy Workshops (EuroS &PW), pp. 40\u201347. IEEE (2024)","DOI":"10.1109\/EuroSPW61312.2024.00012"},{"key":"7_CR20","doi-asserted-by":"crossref","unstructured":"Hannemann, A., \u00dcnal, A.B., Swaminathan, A., Buchmann, E., Akg\u00fcn, M.: A privacy-preserving framework for collaborative machine learning with kernel methods. In: 2023 5th IEEE International Conference on Trust, Privacy and Security in Intelligent Systems and Applications (TPS-ISA), pp. 82\u201390. IEEE (2023)","DOI":"10.1109\/TPS-ISA58951.2023.00020"},{"issue":"1","key":"7_CR21","first-page":"20","volume":"2","author":"B Hasan","year":"2021","unstructured":"Hasan, B., Abdulazeez, A.M.: A review of principal component analysis algorithm for dimensionality reduction. J. Soft Comput. Data Min. 2(1), 20\u201330 (2021)","journal-title":"J. Soft Comput. Data Min."},{"key":"7_CR22","doi-asserted-by":"crossref","unstructured":"LaMontagne, P.J., et\u00a0al.: Oasis-3: longitudinal neuroimaging, clinical, and cognitive dataset for normal aging and alzheimer disease. MedRxiv, pp. 2019\u201312 (2019)","DOI":"10.1101\/2019.12.13.19014902"},{"key":"7_CR23","doi-asserted-by":"publisher","first-page":"147","DOI":"10.1007\/s10115-014-0751-1","volume":"44","author":"KP Lin","year":"2015","unstructured":"Lin, K.P., Chang, Y.W., Chen, M.S.: Secure support vector machines outsourcing with random linear transformation. Knowl. Inf. Syst. 44, 147\u2013176 (2015)","journal-title":"Knowl. Inf. Syst."},{"key":"7_CR24","unstructured":"McMahan, B., Moore, E., Ramage, D., Hampson, S., y\u00a0Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273\u20131282. PMLR (2017)"},{"key":"7_CR25","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":"7_CR26","unstructured":"Mugunthan, V., Polychroniadou, A., Byrd, D., Balch, T.H.: Smpai: secure multi-party computation for federated learning. In: Proceedings of the NeurIPS 2019 Workshop on Robust AI in Financial Services (2019)"},{"key":"7_CR27","doi-asserted-by":"crossref","unstructured":"Sch\u00f6lkopf, B., Smola, A.J.: Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. MIT Press (2002)","DOI":"10.7551\/mitpress\/4175.001.0001"},{"issue":"5","key":"7_CR28","doi-asserted-by":"publisher","first-page":"e148","DOI":"10.1002\/mp.13649","volume":"47","author":"H Seo","year":"2020","unstructured":"Seo, H., et al.: Machine learning techniques for biomedical image segmentation: an overview of technical aspects and introduction to state-of-art applications. Med. Phys. 47(5), e148\u2013e167 (2020)","journal-title":"Med. Phys."},{"key":"7_CR29","unstructured":"Shenggan: Bccd dataset. https:\/\/github.com\/Shenggan\/BCCD_Dataset (2017), Accessed 27 July 2023"},{"key":"7_CR30","unstructured":"Swaminathan, A., Hannemann, A., \u00dcnal, A.B., Pfeifer, N., Akg\u00fcn, M.: Pp-gwas: privacy preserving multi-site genome-wide association studies. arXiv preprint arXiv:2410.08122 (2024)"},{"key":"7_CR31","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"493","DOI":"10.1007\/978-3-030-31578-8_27","volume-title":"Cryptology and Network Security","author":"AB \u00dcnal","year":"2019","unstructured":"\u00dcnal, A.B., Akg\u00fcn, M., Pfeifer, N.: A framework with randomized encoding for a fast privacy preserving calculation of non-linear kernels for machine learning applications in precision medicine. In: Mu, Y., Deng, R.H., Huang, X. (eds.) CANS 2019. LNCS, vol. 11829, pp. 493\u2013511. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-31578-8_27"},{"key":"7_CR32","doi-asserted-by":"crossref","unstructured":"\u00dcnal, A.B., Akg\u00fcn, M., Pfeifer, N.: Escaped: Efficient secure and private dot product framework for kernel-based machine learning algorithms with applications in healthcare. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol.\u00a035, pp. 9988\u20139996 (2021)","DOI":"10.1609\/aaai.v35i11.17199"},{"key":"7_CR33","unstructured":"Williams, C., Rasmussen, C.: Gaussian processes for regression. In: Advances in Neural Information Processing Systems, vol. 8 (1995)"},{"key":"7_CR34","doi-asserted-by":"crossref","unstructured":"Yao, A.C.: Protocols for secure computations. In: 23rd Annual Symposium on Foundations of Computer Science (sfcs 1982), pp. 160\u2013164. IEEE (1982)","DOI":"10.1109\/SFCS.1982.38"},{"key":"7_CR35","doi-asserted-by":"crossref","unstructured":"Zeng, Z.Q., Yu, H.B., Xu, H.R., Xie, Y.Q., Gao, J.: Fast training support vector machines using parallel sequential minimal optimization. In: 2008 3rd International Conference on Intelligent System and Knowledge Engineering, vol.\u00a01, pp. 997\u20131001. IEEE (2008)","DOI":"10.1109\/ISKE.2008.4731075"},{"key":"7_CR36","unstructured":"Zhang, C., Li, S., Xia, J., Wang, W., Yan, F., Liu, Y.: Batchcrypt: efficient homomorphic encryption for cross-silo federated learning. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC 2020) (2020)"}],"container-title":["Lecture Notes in Computer Science","Computational Intelligence Methods for Bioinformatics and Biostatistics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-89704-7_7","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,5,14]],"date-time":"2025-05-14T11:28:31Z","timestamp":1747222111000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-89704-7_7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9783031897030","9783031897047"],"references-count":36,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-89704-7_7","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":"15 May 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"CIBB","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Benevento","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Italy","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":"4 September 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6 September 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"cibb2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/cibb2024.unisannio.it","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}