{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,3]],"date-time":"2025-11-03T13:48:17Z","timestamp":1762177697694,"version":"3.40.3"},"publisher-location":"Cham","reference-count":38,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031611391"},{"type":"electronic","value":"9783031611407"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"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":[[2024]]},"DOI":"10.1007\/978-3-031-61140-7_28","type":"book-chapter","created":{"date-parts":[[2024,5,30]],"date-time":"2024-05-30T07:10:33Z","timestamp":1717053033000},"page":"282-299","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Comparison of\u00a0an\u00a0Accelerated Garble Embedding Methodology for\u00a0Privacy Preserving in\u00a0Biomedical Data Analytics"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2194-1112","authenticated-orcid":false,"given":"Nikola","family":"Hristov-Kalamov","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8325-5372","authenticated-orcid":false,"given":"Ra\u00fal","family":"Fern\u00e1ndez-Ruiz","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3387-6709","authenticated-orcid":false,"given":"Agust\u00edn","family":"\u00e1lvarez-Marquina","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5575-4342","authenticated-orcid":false,"given":"Esther","family":"N\u00fa\u00f1ez-Vidal","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0909-7585","authenticated-orcid":false,"given":"Francisco","family":"Dom\u00ednguez-Mateos","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6063-4898","authenticated-orcid":false,"given":"Daniel","family":"Palacios-Alonso","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,5,31]]},"reference":[{"key":"28_CR1","doi-asserted-by":"crossref","unstructured":"Applebaum, B., Damg\u00e5rd, I., Ishai, Y., Nielsen, M., Zichron, L.: Secure arithmetic computation with constant computational overhead. Cryptology ePrint Archive, Paper 2017\/617 (2017)","DOI":"10.1007\/978-3-319-63688-7_8"},{"key":"28_CR2","doi-asserted-by":"publisher","unstructured":"Asharov, G., Lindell, Y., Schneider, T., Zohner, M.: More efficient oblivious transfer and extensions for faster secure computation. In: Proceedings of the 2013 ACM SIGSAC Conference on Computer & Communications Security, pp. 535\u2013548. CCS \u201913, Association for Computing Machinery, New York, NY, USA (2013). https:\/\/doi.org\/10.1145\/2508859.2516738","DOI":"10.1145\/2508859.2516738"},{"key":"28_CR3","unstructured":"Ball, M., Carmer, B., Malkin, T., Rosulek, M., Schimanski, N.: Garbled neural networks are practical. IACR Cryptol. ePrint Arch. p.\u00a0338 (2019)"},{"key":"28_CR4","doi-asserted-by":"publisher","unstructured":"Ball, M., Malkin, T., Rosulek, M.: Garbling gadgets for Boolean and arithmetic circuits. In: Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security, pp. 565\u2013577. CCS \u201916, Association for Computing Machinery, New York, NY, USA (2016). https:\/\/doi.org\/10.1145\/2976749.2978410","DOI":"10.1145\/2976749.2978410"},{"key":"28_CR5","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"97","DOI":"10.1007\/3-540-44750-4_8","volume-title":"Advances in Cryptology \u2014 CRYPT0\u2019 95","author":"D Beaver","year":"1995","unstructured":"Beaver, D.: Precomputing oblivious transfer. In: Coppersmith, D. (ed.) CRYPTO 1995. LNCS, vol. 963, pp. 97\u2013109. Springer, Heidelberg (1995). https:\/\/doi.org\/10.1007\/3-540-44750-4_8"},{"key":"28_CR6","doi-asserted-by":"publisher","unstructured":"Bellare, M., Hoang, V., Keelveedhi, S., Rogaway, P.: Efficient garbling from a fixed-key blockcipher. In: 2012 IEEE Symposium on Security and Privacy, pp. 478\u2013492. IEEE Computer Society, Los Alamitos, CA, USA (2013). https:\/\/doi.org\/10.1109\/SP.2013.39","DOI":"10.1109\/SP.2013.39"},{"key":"28_CR7","doi-asserted-by":"publisher","unstructured":"Boyle, E., et al.: Efficient two-round OT extension and silent non-interactive secure computation. In: Proceedings of the 2019 ACM SIGSAC Conference on Computer and Communications Security, pp. 291\u2013308. CCS \u201919, Association for Computing Machinery, New York, NY, USA (2019). https:\/\/doi.org\/10.1145\/3319535.3354255","DOI":"10.1145\/3319535.3354255"},{"key":"28_CR8","unstructured":"Breebaart, J., Busch, C., Grave, J., Kindt, E.: A reference architecture for biometric template protection based on pseudo identities. In: ASDFASD, pp. 25\u201338 (2008)"},{"key":"28_CR9","doi-asserted-by":"publisher","unstructured":"Chen, T., et al.: THE-X: privacy-preserving transformer inference with homomorphic encryption. In: Muresan, S., Nakov, P., Villavicencio, A. (eds.) Findings of the Association for Computational Linguistics: ACL 2022, pp. 3510\u20133520. Association for Computational Linguistics, Dublin, Ireland (2022). https:\/\/doi.org\/10.18653\/v1\/2022.findings-acl.277","DOI":"10.18653\/v1\/2022.findings-acl.277"},{"key":"28_CR10","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"409","DOI":"10.1007\/978-3-319-70694-8_15","volume-title":"Advances in Cryptology \u2013 ASIACRYPT 2017","author":"JH Cheon","year":"2017","unstructured":"Cheon, J.H., Kim, A., Kim, M., Song, Y.: Homomorphic encryption for arithmetic of approximate numbers. In: Takagi, T., Peyrin, T. (eds.) ASIACRYPT 2017. LNCS, vol. 10624, pp. 409\u2013437. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-70694-8_15"},{"issue":"2","key":"28_CR11","doi-asserted-by":"publisher","DOI":"10.2196\/12702","volume":"7","author":"FK Dankar","year":"2019","unstructured":"Dankar, F.K., Madathil, N., Dankar, S.K., Boughorbel, S.: Privacy-preserving analysis of distributed biomedical data: designing efficient and secure multiparty computations using distributed statistical learning theory. JMIR Med. Inform. 7(2), e12702 (2019)","journal-title":"JMIR Med. Inform."},{"issue":"1","key":"28_CR12","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12911-020-1041-3","volume":"20","author":"J Eicher","year":"2020","unstructured":"Eicher, J., Bild, R., Spengler, H., Kuhn, K.A., Prasser, F.: A comprehensive tool for creating and evaluating privacy-preserving biomedical prediction models. BMC Med. Inform. Decis. Mak. 20(1), 1\u201314 (2020)","journal-title":"BMC Med. Inform. Decis. Mak."},{"key":"28_CR13","unstructured":"El\u00a0Emam, K., Arbuckle, L.: Anonymizing health data: case studies and methods to get you started. O\u2019Reilly Media, Inc. (2013)"},{"key":"28_CR14","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"8","DOI":"10.1007\/0-387-34799-2_2","volume-title":"Advances in Cryptology \u2014 CRYPTO\u2019 88","author":"R Impagliazzo","year":"1990","unstructured":"Impagliazzo, R., Rudich, S.: Limits on the provable consequences of one-way permutations. In: Goldwasser, S. (ed.) CRYPTO 1988. LNCS, vol. 403, pp. 8\u201326. Springer, New York (1990). https:\/\/doi.org\/10.1007\/0-387-34799-2_2"},{"key":"28_CR15","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"145","DOI":"10.1007\/978-3-540-45146-4_9","volume-title":"Advances in Cryptology - CRYPTO 2003","author":"Y Ishai","year":"2003","unstructured":"Ishai, Y., Kilian, J., Nissim, K., Petrank, E.: Extending oblivious transfers efficiently. In: Boneh, D. (ed.) CRYPTO 2003. LNCS, vol. 2729, pp. 145\u2013161. Springer, Heidelberg (2003). https:\/\/doi.org\/10.1007\/978-3-540-45146-4_9"},{"issue":"6245","key":"28_CR16","doi-asserted-by":"publisher","first-page":"255","DOI":"10.1126\/science.aaa8415","volume":"349","author":"MI Jordan","year":"2015","unstructured":"Jordan, M.I., Mitchell, T.M.: Machine learning: trends, perspectives, and prospects. Science 349(6245), 255\u2013260 (2015)","journal-title":"Science"},{"key":"28_CR17","doi-asserted-by":"publisher","unstructured":"Kim, D., Lee, G., Oh, S.: Toward privacy-preserving text embedding similarity with homomorphic encryption. In: Chen, C.C., Huang, H.H., Takamura, H., Chen, H.H. (eds.) Proceedings of the Fourth Workshop on Financial Technology and Natural Language Processing (FinNLP), pp. 25\u201336. Association for Computational Linguistics, Abu Dhabi, United Arab Emirates (Hybrid) (2022). https:\/\/doi.org\/10.18653\/v1\/2022.finnlp-1.4","DOI":"10.18653\/v1\/2022.finnlp-1.4"},{"key":"28_CR18","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/978-3-642-10433-6_1","volume-title":"Cryptology and Network Security","author":"V Kolesnikov","year":"2009","unstructured":"Kolesnikov, V., Sadeghi, A.-R., Schneider, T.: Improved garbled circuit building blocks and applications to auctions and computing minima. In: Garay, J.A., Miyaji, A., Otsuka, A. (eds.) CANS 2009. LNCS, vol. 5888, pp. 1\u201320. Springer, Heidelberg (2009). https:\/\/doi.org\/10.1007\/978-3-642-10433-6_1"},{"key":"28_CR19","unstructured":"Lam, M., Mitzenmacher, M., Reddi, V.J., Wei, G.Y., Brooks, D.: Tabula: Efficiently computing nonlinear activation functions for secure neural network inference (2022)"},{"key":"28_CR20","doi-asserted-by":"publisher","unstructured":"Lee, G., Kim, M., Park, J.H., Hwang, S.W., Cheon, J.H.: Privacy-preserving text classification on BERT embeddings with homomorphic encryption. In: Carpuat, M., de\u00a0Marneffe, M.C., Meza\u00a0Ruiz, I.V. (eds.) Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 3169\u20133175. Association for Computational Linguistics, Seattle, United States (2022). https:\/\/doi.org\/10.18653\/v1\/2022.naacl-main.231","DOI":"10.18653\/v1\/2022.naacl-main.231"},{"issue":"1","key":"28_CR21","doi-asserted-by":"publisher","first-page":"2","DOI":"10.1136\/amiajnl-2012-001509","volume":"20","author":"BA Malin","year":"2013","unstructured":"Malin, B.A., Emam, K.E., O\u2019Keefe, C.M.: Biomedical data privacy: problems, perspectives, and recent advances. J. Am. Med. Inform. Assoc. 20(1), 2\u20136 (2013)","journal-title":"J. Am. Med. Inform. Assoc."},{"key":"28_CR22","doi-asserted-by":"crossref","unstructured":"Narayanan, A., Shmatikov, V.: Robust de-anonymization of large sparse datasets. In: 2008 IEEE Symposium on Security and Privacy (2008), pp. 111\u2013125. IEEE (2008)","DOI":"10.1109\/SP.2008.33"},{"key":"28_CR23","doi-asserted-by":"publisher","unstructured":"Nautsch, A., Isadskiy, S., Kolberg, J., Gomez-Barrero, M., Busch, C.: Homomorphic Encryption for speaker recognition: protection of biometric templates and vendor model parameters. In: Proceedings the Speaker and Language Recognition Workshop (2018), pp. 16\u201323 (2018). https:\/\/doi.org\/10.21437\/Odyssey.2018-3","DOI":"10.21437\/Odyssey.2018-3"},{"key":"28_CR24","doi-asserted-by":"crossref","unstructured":"O\u2019herrin, J.K., Fost, N., Kudsk, K.A.: Health insurance portability accountability act (HIPAA) regulations: effect on medical record research. Ann. Surg. 239(6), 772 (2004)","DOI":"10.1097\/01.sla.0000128307.98274.dc"},{"key":"28_CR25","unstructured":"Palacios-Alonso, D., et al.: Privacidad por dise\u00f1o, clave para la buena gobernanza. Derecom, pp. 215\u2013223 (2021)"},{"key":"28_CR26","doi-asserted-by":"publisher","first-page":"602","DOI":"10.1007\/978-3-031-38551-3_19","volume-title":"Advances in Cryptology - CRYPTO 2023","author":"S Raghuraman","year":"2023","unstructured":"Raghuraman, S., Rindal, P., Tanguy, T.: Expand-convolute codes for pseudorandom correlation generators from LPN. In: Handschuh, H., Lysyanskaya, A. (eds.) Advances in Cryptology - CRYPTO 2023, pp. 602\u2013632. Springer Nature Switzerland, Cham (2023). https:\/\/doi.org\/10.1007\/978-3-031-38551-3_19"},{"key":"28_CR27","first-page":"1","volume":"25","author":"P Regulation","year":"2018","unstructured":"Regulation, P.: General data protection regulation. Intouch 25, 1\u20135 (2018)","journal-title":"Intouch"},{"key":"28_CR28","doi-asserted-by":"crossref","unstructured":"Shokri, R., Shmatikov, V.: Privacy-preserving deep learning. In: Proceedings of the 22nd ACM SIGSAC Conference on Computer and Communications Security, pp. 1310\u20131321 (2015)","DOI":"10.1145\/2810103.2813687"},{"key":"28_CR29","doi-asserted-by":"crossref","unstructured":"Shokri, R., Stronati, M., Song, C., Shmatikov, V.: Membership inference attacks against machine learning models. In: 2017 IEEE Symposium on Security and Privacy (SP), pp. 3\u201318. IEEE (2017)","DOI":"10.1109\/SP.2017.41"},{"key":"28_CR30","unstructured":"Sweeney, L.: Computational disclosure control: A primer on data privacy protection. Ph.D. thesis, Massachusetts Institute of Technology (2001)"},{"key":"28_CR31","doi-asserted-by":"crossref","unstructured":"Vladimir\u00a0Kolesnikov, T.S.: Improved garbled circuit: free XOR gates and applications. In: ICALP \u201908: Proceedings of the 35th International Colloquium on Automata, Languages and Programming, Part II, pp. 486\u2013498 (2008)","DOI":"10.1007\/978-3-540-70583-3_40"},{"issue":"5","key":"28_CR32","doi-asserted-by":"publisher","first-page":"1029","DOI":"10.1093\/jamia\/ocv004","volume":"22","author":"W Xia","year":"2015","unstructured":"Xia, W., Heatherly, R., Ding, X., Li, J., Malin, B.A.: Ru policy frontiers for health data de-identification. J. Am. Med. Inform. Assoc. 22(5), 1029\u20131041 (2015)","journal-title":"J. Am. Med. Inform. Assoc."},{"key":"28_CR33","doi-asserted-by":"crossref","unstructured":"Xiang, D., Cai, W., et\u00a0al.: Privacy protection and secondary use of health data: strategies and methods. BioMed Res. Int. 2021, 6967166 (2021)","DOI":"10.1155\/2021\/6967166"},{"key":"28_CR34","doi-asserted-by":"publisher","unstructured":"Yang, K., Weng, C., Lan, X., Zhang, J., Wang, X.: Ferret: fast extension for correlated OT with small communication. In: Proceedings of the 2020 ACM SIGSAC Conference on Computer and Communications Security, pp. 1607\u20131626. CCS \u201920, Association for Computing Machinery, New York, NY, USA (2020). https:\/\/doi.org\/10.1145\/3372297.3417276","DOI":"10.1145\/3372297.3417276"},{"key":"28_CR35","doi-asserted-by":"publisher","unstructured":"Yao, A.C.C.: How to generate and exchange secrets. In: 27th Annual Symposium on Foundations of Computer Science (SFCS 1986), pp. 162\u2013167 (1986). https:\/\/doi.org\/10.1109\/SFCS.1986.25","DOI":"10.1109\/SFCS.1986.25"},{"key":"28_CR36","doi-asserted-by":"publisher","unstructured":"Yu, X., Chen, X., Shi, J.: Vector based privacy-preserving document similarity with LSA. In: 2017 IEEE 9th International Conference on Communication Software and Networks (ICCSN), pp. 1383\u20131387 (2017). https:\/\/doi.org\/10.1109\/ICCSN.2017.8230336","DOI":"10.1109\/ICCSN.2017.8230336"},{"key":"28_CR37","doi-asserted-by":"crossref","unstructured":"Zahur, S., Rosulek, M., Evans, D.: Two halves make a whole: Reducing data transfer in garbled circuits using half gates. Cryptology ePrint Archive, Paper 2014\/756 (2014)","DOI":"10.1007\/978-3-662-46803-6_8"},{"key":"28_CR38","doi-asserted-by":"publisher","unstructured":"Zhou, J., Li, J., Panaousis, E., Liang, K.: Deep binarized convolutional neural network inferences over encrypted data. In: 2020 7th IEEE International Conference on Cyber Security and Cloud Computing (CSCloud)\/2020 6th IEEE International Conference on Edge Computing and Scalable Cloud (EdgeCom), pp. 160\u2013167 (2020). https:\/\/doi.org\/10.1109\/CSCloud-EdgeCom49738.2020.00035","DOI":"10.1109\/CSCloud-EdgeCom49738.2020.00035"}],"container-title":["Lecture Notes in Computer Science","Artificial Intelligence for Neuroscience and Emotional Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-61140-7_28","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,20]],"date-time":"2024-11-20T18:12:51Z","timestamp":1732126371000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-61140-7_28"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031611391","9783031611407"],"references-count":38,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-61140-7_28","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"31 May 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"IWINAC","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Work-Conference on the Interplay Between Natural and Artificial Computation","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Olh\u00e2o","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Portugal","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":"31 May 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"3 June 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":"iwinac2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/iwinac.eu\/iwinac.org\/iwinac2024\/index.html","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}