{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,3]],"date-time":"2025-07-03T12:10:05Z","timestamp":1751544605552,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":39,"publisher":"ACM","content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2025,8,25]]},"DOI":"10.1145\/3709020.3735357","type":"proceedings-article","created":{"date-parts":[[2025,7,3]],"date-time":"2025-07-03T11:28:01Z","timestamp":1751542081000},"page":"1-12","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Invited Paper: A Practical Framework for Encrypted Medical Prediction using Blockchain-Anchored Key Management System"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4253-9285","authenticated-orcid":false,"given":"Rafik","family":"Hamza","sequence":"first","affiliation":[{"name":"Tokyo International University, Toshima, Tokyo, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3044-8175","authenticated-orcid":false,"given":"Minh-Son","family":"Dao","sequence":"additional","affiliation":[{"name":"NICT, Koganei, Tokyo, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2025,8,24]]},"reference":[{"key":"e_1_3_3_1_2_2","doi-asserted-by":"crossref","unstructured":"Acar A. Aksu H. Uluagac A. S. & Conti M. (2018). A survey on homomorphic encryption schemes: Theory and implementation. ACM Computing Surveys (CSUR) 51(4) 1-35.","DOI":"10.1145\/3214303"},{"key":"e_1_3_3_1_3_2","doi-asserted-by":"crossref","unstructured":"Albrecht M. R. Player R. & Scott S. (2018). On the concrete hardness of learning with errors. Journal of Mathematical Cryptology 9(3) 169-203.","DOI":"10.1515\/jmc-2015-0016"},{"key":"e_1_3_3_1_4_2","doi-asserted-by":"crossref","unstructured":"Al-Rubaie M. & Chang J. M. (2019). Privacy-preserving machine learning: Threats and solutions. IEEE Security & Privacy 17(2) 49-58.","DOI":"10.1109\/MSEC.2018.2888775"},{"key":"e_1_3_3_1_5_2","doi-asserted-by":"crossref","unstructured":"Annas G. J. (2003). HIPAA regulations-a new era of medical-record privacy? New England Journal of Medicine 348(15) 1486-1490.","DOI":"10.1056\/NEJMlim035027"},{"key":"e_1_3_3_1_6_2","unstructured":"AWS. (2020). AWS Key Management Service. https:\/\/aws.amazon.com\/kms\/"},{"key":"e_1_3_3_1_7_2","unstructured":"Azure. (2020). Azure Key Vault. https:\/\/azure.microsoft.com\/en-us\/services\/key-vault\/"},{"key":"e_1_3_3_1_8_2","doi-asserted-by":"crossref","unstructured":"Bergamaschi F. Halevi S. Halevi T. T. & Hunt H. (2019). Homomorphic training of 30 000 logistic regression models. In International Conference on Applied Cryptography and Network Security (pp. 592-611). Springer.","DOI":"10.1007\/978-3-030-21568-2_29"},{"key":"e_1_3_3_1_9_2","doi-asserted-by":"crossref","unstructured":"Boockholdt J. L. (1989). Implementing security and integrity in micro-mainframe networks. MIS Quarterly 13(2) 135-144.","DOI":"10.2307\/248920"},{"key":"e_1_3_3_1_10_2","doi-asserted-by":"crossref","unstructured":"Bost R. Popa R. A. Tu S. & Goldwasser S. (2015). Machine learning classification over encrypted data. In Network and Distributed System Security Symposium (NDSS).","DOI":"10.14722\/ndss.2015.23241"},{"key":"e_1_3_3_1_11_2","doi-asserted-by":"crossref","unstructured":"Cheon J. H. Kim A. Kim M. & Song Y. (2017). Homomorphic encryption for arithmetic of approximate numbers. In International Conference on the Theory and Application of Cryptology and Information Security (pp. 409-437). Springer.","DOI":"10.1007\/978-3-319-70694-8_15"},{"key":"e_1_3_3_1_12_2","doi-asserted-by":"crossref","unstructured":"Crawford J. L. Gentry C. Halevi S. Platt D. & Shoup V. (2018). Doing real work with FHE: The case of logistic regression. In Workshop on Theory and Practice of Secure Multi-Party Computation (pp. 1-12).","DOI":"10.1145\/3267973.3267974"},{"key":"e_1_3_3_1_13_2","doi-asserted-by":"crossref","unstructured":"Dwork C. & Roth A. (2014). The algorithmic foundations of differential privacy. Foundations and Trends in Theoretical Computer Science 9(3-4) 211-407.","DOI":"10.1561\/0400000042"},{"key":"e_1_3_3_1_14_2","doi-asserted-by":"crossref","unstructured":"Fern\u00e1ndez-Alem\u00e1n J. L. Se\u00f1or I. C. Lozoya P. \u00c1. O. & Toval A. (2013). Security and privacy in electronic health records: A systematic literature review. Journal of Biomedical Informatics 46(3) 541-562.","DOI":"10.1016\/j.jbi.2012.12.003"},{"key":"e_1_3_3_1_15_2","doi-asserted-by":"crossref","unstructured":"Gentry C. Halevi S. & Smart N. P. (2012). Homomorphic evaluation of the AES circuit. In Annual Cryptology Conference (pp. 850-867). Springer.","DOI":"10.1007\/978-3-642-32009-5_49"},{"key":"e_1_3_3_1_16_2","doi-asserted-by":"crossref","unstructured":"Halevi S. & Shoup V. (2014). Algorithms in HElib. In Annual Cryptology Conference (pp. 554-571). Springer.","DOI":"10.1007\/978-3-662-44371-2_31"},{"key":"e_1_3_3_1_17_2","unstructured":"Han K. Hong S. Cheon J. H. & Park D. (2018). Logistic regression on homomorphic encrypted data at scale. In AAAI Conference on Artificial Intelligence (pp. 7198-7205)."},{"key":"e_1_3_3_1_18_2","unstructured":"Han K. Hong S. Cheon J. H. & Park D. (2019). Efficient logistic regression on large encrypted data. IACR Transactions on Symmetric Cryptology 2019(1) 173-203."},{"key":"e_1_3_3_1_19_2","unstructured":"HEAAN. (2018). Homomorphic Encryption for Arithmetic of Approximate Numbers. https:\/\/github.com\/snucrypto\/HEAAN"},{"key":"e_1_3_3_1_20_2","doi-asserted-by":"crossref","unstructured":"Kim M. Song Y. Wang S. Xia Y. & Jiang X. (2018). Secure logistic regression based on homomorphic encryption. In AMIA Annual Symposium Proceedings (pp. 606-615).","DOI":"10.2196\/preprints.8805"},{"key":"e_1_3_3_1_21_2","doi-asserted-by":"crossref","unstructured":"Lindner R. & Peikert C. (2011). Better key sizes (and attacks) for LWE-based encryption. In Cryptographers\u2019 Track at the RSA Conference (pp. 319-339). Springer.","DOI":"10.1007\/978-3-642-19074-2_21"},{"key":"e_1_3_3_1_22_2","unstructured":"McMahan H. B. Moore E. Ramage D. Hampson S. & Arcas B. A. (2017). Communication-efficient learning of deep networks from decentralized data. In International Conference on Artificial Intelligence and Statistics (pp. 1273-1282)."},{"key":"e_1_3_3_1_23_2","unstructured":"National Institute of Standards and Technology. (2020). Post-Quantum Cryptography Standardization. https:\/\/csrc.nist.gov\/Projects\/post-quantum-cryptography"},{"key":"e_1_3_3_1_24_2","doi-asserted-by":"crossref","unstructured":"Price W. N. & Cohen I. G. (2019). Privacy in the age of medical big data. Nature Medicine 25(1) 37-43.","DOI":"10.1038\/s41591-018-0272-7"},{"key":"e_1_3_3_1_25_2","unstructured":"SEAL. (2020). Microsoft SEAL: Simple Encrypted Arithmetic Library. https:\/\/github.com\/Microsoft\/SEAL"},{"key":"e_1_3_3_1_26_2","unstructured":"TenSEAL. (2020). A library for doing homomorphic encryption operations on tensors. https:\/\/github.com\/OpenMined\/TenSEAL"},{"key":"e_1_3_3_1_27_2","doi-asserted-by":"crossref","unstructured":"P. Sathishkumar K. Pugalarasan C. Ponnparamaguru and M. Vasanthkumar \u201cImproving healthcare data security using Cheon-Kim-Kim-Song (CKKS) homomorphic encryption \u201d in Proc. 2024 Int. Conf. Knowledge Engineering and Communication Systems (ICKECS)* vol. 1 pp. 1\u20136 2024 IEEE.","DOI":"10.1109\/ICKECS61492.2024.10616691"},{"key":"e_1_3_3_1_28_2","doi-asserted-by":"crossref","unstructured":"V. V. L. Divakar Allavarpu V. S. Naresh and A. Krishna Mohan \u201cPrivacy-preserving credit risk analysis based on homomorphic encryption aware logistic regression in the cloud \u201d *Security and Privacy* vol. 7 no. 3 p. e372 2024 Wiley Online Library.","DOI":"10.1002\/spy2.372"},{"key":"e_1_3_3_1_29_2","doi-asserted-by":"crossref","unstructured":"W. Miao and W. Wu \u201cPrivacy-Preserving Logistic Regression Model Training Scheme by Homomorphic Encryption \u201d in Proc. Int. Conf. Information and Communications Security* pp. 271\u2013291 2024 Springer.","DOI":"10.1007\/978-981-97-8798-2_14"},{"key":"e_1_3_3_1_30_2","doi-asserted-by":"crossref","unstructured":"S. Chen J. Li K. Zhang A. Di and M. Lu \u201cPrivacy-Preserving Breast Cancer Prediction Based on Logistic Regression \u201d *The Computer Journal* vol. 67 no. 8 pp. 2667\u20132676 2024.","DOI":"10.1093\/comjnl\/bxae035"},{"key":"e_1_3_3_1_31_2","doi-asserted-by":"crossref","unstructured":"C. Niu Z. Huang Z. Yang Y. Chen L. Kong C. Hong and T. Wei \u201cXBOOT: Free-XOR Gates for CKKS with Applications to Transciphering \u201d *Cryptology ePrint Archive* 2025.","DOI":"10.46586\/tches.v2025.i3.583-613"},{"key":"e_1_3_3_1_32_2","doi-asserted-by":"crossref","unstructured":"A. Bilzhause M. Huber H. C. P\u00f6hls and K. Samelin \u201cCryptographically enforced four-eyes principle \u201d in Proc. 11th Int. Conf. Availability Reliability and Security (ARES)* pp. 760\u2013767 2016 IEEE.","DOI":"10.1109\/ARES.2016.28"},{"key":"e_1_3_3_1_33_2","doi-asserted-by":"crossref","unstructured":"T. Wu et al. \u201cPPCA: Privacy-Preserving Continuous Authentication Scheme With Consistency Proof for Zero-Trust Architecture Networks \u201d IEEE Internet of Things Journal 2025.","DOI":"10.1109\/JIOT.2025.3537980"},{"key":"e_1_3_3_1_34_2","doi-asserted-by":"crossref","unstructured":"P. Zhang J. White D. C. Schmidt G. Lenz and S. T. Rosenbloom \u201cFHIRChain: Applying Blockchain to Securely and Scalably Share Clinical Data \u201d *Comput. Struct. Biotechnol. J.* vol. 16 pp. 267\u2013278 2018.","DOI":"10.1016\/j.csbj.2018.07.004"},{"key":"e_1_3_3_1_35_2","unstructured":"Wang S. Zhang Y. Zhang W. Wang X. Liu Y. & Dai H. (2021). Blockchain-Empowered Federated Learning for Healthcare Meta-Verses: A Survey. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2111.03437."},{"key":"e_1_3_3_1_36_2","doi-asserted-by":"crossref","unstructured":"A. Azaria A. Ekblaw T. Vieira and A. Lippman \u201cMedRec: Using Blockchain for Medical Data Access and Permission Management \u201d in Proc. 2nd Int. Conf. Open and Big Data (OBD)* pp. 25\u201330 2016.","DOI":"10.1109\/OBD.2016.11"},{"key":"e_1_3_3_1_37_2","unstructured":"S. Nakamoto \u201cBitcoin: A Peer-to-Peer Electronic Cash System \u201d 2008. [Online]. Available: https:\/\/bitcoin.org\/bitcoin.pdf"},{"key":"e_1_3_3_1_38_2","doi-asserted-by":"crossref","unstructured":"E. Androulaki A. Barger A. Bessani and others \u201cHyperledger Fabric: A Distributed Operating System for Permissioned Blockchains \u201d in Proc. 13th EuroSys Conf.* pp. 1\u201315 2018.","DOI":"10.1145\/3190508.3190538"},{"key":"e_1_3_3_1_39_2","unstructured":"N. Morgan et al.* \u201cQuorum: A Permissioned Ethereum Blockchain \u201d 2016. [Online]. Available: https:\/\/github.com\/jpmorganchase\/quorum."},{"key":"e_1_3_3_1_40_2","unstructured":"G. Wood \u201cEthereum: A Secure Decentralized Generalized Transaction Ledger \u201d 2014. [Online]. Available: https:\/\/ethereum.org\/en\/whitepaper\/"}],"event":{"name":"SCID '25: The 2nd Workshop on Security-Centric Strategies for Combating Information Disorder","sponsor":["SIGSAC ACM Special Interest Group on Security, Audit, and Control"],"location":"Hanoi Vietnam","acronym":"SCID '25"},"container-title":["Proceedings of the 2nd Workshop on Security-Centric Strategies for Combating Information Disorder"],"original-title":[],"deposited":{"date-parts":[[2025,7,3]],"date-time":"2025-07-03T11:28:14Z","timestamp":1751542094000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3709020.3735357"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,24]]},"references-count":39,"alternative-id":["10.1145\/3709020.3735357","10.1145\/3709020"],"URL":"https:\/\/doi.org\/10.1145\/3709020.3735357","relation":{},"subject":[],"published":{"date-parts":[[2025,8,24]]},"assertion":[{"value":"2025-08-24","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}