AI-DRIVEN HYBRID ENCRYPTION FOR SECURE ELECTRONIC  MEDICAL RECORDS

Edy Prayitno; Basuki Heri Winarno; Sri Setyowati; Sutono Sutono; Riyadi Riyadi

doi:10.33330/jurteksi.v12i2.4142

Edy Prayitno Universitas Teknologi Digital Indonesia
Basuki Heri Winarno Universitas Teknologi Digital Indonesia
Sri Setyowati Sekolah Tinggi Ilmu Kesehatan Surya Global
Sutono Sutono Sekolah Tinggi Ilmu Kesehatan Surya Global
Riyadi Riyadi Universitas Teknologi Digital Indonesia

DOI: https://doi.org/10.33330/jurteksi.v12i2.4142

Keywords: AI key management; attack resistance; encryption performance; electronic medical records; hybrid encryption

Abstract

Abstract: In the era of sensitive health data and frequent cyberattacks, securing electronic medical records (EMR) has become a critical challenge. This study proposes a hybrid encryption framework combining Affine and AES algorithms with an AI-based key management module to enhance EMR security while maintaining efficiency. A dataset of 1,000 simulated records was evaluated using five cryptographic configurations: Affine-only, AES-only, RSA-only, Affine–AES, and Affine–AES with AI. Performance was measured through encryption/decryption latency and ciphertext size, while security was assessed under brute-force, SQL injection, and phishing simulations. The AI decision tree for key generation was evaluated using accuracy, precision, recall, F1-score, and entropy metrics. Results show that the AI-enhanced hybrid method eliminates brute-force success, introduces only minor latency overhead, and generates high-entropy keys with reliability above 98%. These findings indicate that integrating AI-based dynamic key regeneration into hybrid encryption can improve EMR security while remaining practical for clinical and cloud-based healthcare systems. Future work should involve real clinical datasets and explore post-quantum cryptographic extensions.

Keywords: AI key management; attack resistance; encryption performance; electronic medical records; hybrid encryption

Abstrak: Di era meningkatnya sensitivitas data kesehatan dan maraknya serangan siber, perlindungan Rekam Medis Elektronik (RME) menjadi tantangan penting. Penelitian ini mengusulkan kerangka enkripsi hibrida yang menggabungkan algoritma Affine dan AES dengan modul manajemen kunci berbasis AI untuk meningkatkan keamanan RME tanpa mengorbankan efisiensi. Dataset simulasi berisi 1.000 entri diuji menggunakan lima konfigurasi kriptografi: Affine-only, AES-only, RSA-only, Affine–AES, serta Affine–AES dengan AI. Performa diukur melalui latensi enkripsi/dekripsi dan ukuran ciphertext, sedangkan keamanan dievaluasi melalui simulasi serangan brute force, SQL injection, dan phishing. Model decision tree untuk manajemen kunci dinilai menggunakan metrik akurasi, presisi, recall, F1-score, dan entropi. Hasil menunjukkan bahwa metode hibrida dengan AI menghilangkan keberhasilan brute force, menambah overhead latensi yang minimal, serta menghasilkan kunci berentropi tinggi dengan reliabilitas di atas 98%. Temuan ini menunjukkan bahwa regenerasi kunci dinamis berbasis AI dalam skema enkripsi hibrida dapat meningkatkan keamanan RME sekaligus tetap praktis untuk sistem klinis dan layanan kesehatan berbasis cloud. Penelitian selanjutnya disarankan menggunakan dataset klinis nyata dan mengeksplorasi kriptografi pascakuantum.

Kata kunci: enkripsi hibrida; ketahanan serangan; kinerja enkripsi; manajemen kunci berbasis AI; rekam medis elektronik

References

[1] F. Wurster et al., “The Analyzation of Change in Documentation due to the Introduction of Electronic Pa-tient Records in Hospitals—A Sys-tematic Review,” J Med Syst, vol. 46, no. 8, p. 54, Aug. 2022, doi: 10.1007/s10916-022-01840-0.
[2] C. A. Rhoades, B. E. Whitacre, and A. F. Davis, “Higher Electronic Health Record Functionality Is As-sociated with Lower Operating Costs in Urban—but Not Rural—Hospitals,” Appl Clin Inform, vol. 13, no. 03, pp. 665–676, May 2022, doi: 10.1055/s-0042-1750415.
[3] L. B. Russell et al., “The Electronic Health Record as the Primary Data Source in a Pragmatic Trial: A Case Study,” Med Decis Making, vol. 42, no. 8, pp. 975–984, Nov. 2022, doi: 10.1177/0272989X211069980.
[4] H. Zheng and S. Jiang, “Frequent and diverse use of electronic health records in the United States: A trend analysis of national surveys,” DIGITAL HEALTH, vol. 8, p. 205520762211128, Jan. 2022, doi: 10.1177/20552076221112840.
[5] T. C. Kariotis, M. Prictor, S. Chang, and K. Gray, “Impact of Electronic Health Records on Information Practices in Mental Health Con-texts: Scoping Review,” J Med In-ternet Res, vol. 24, no. 5, p. e30405, May 2022, doi: 10.2196/30405.
[6] J. Kosteniuk et al., “Factors identi-fied as barriers or facilitators to EMR/EHR based interprofessional primary care: a scoping review,” Journal of Interprofessional Care, vol. 38, no. 2, pp. 319–330, Mar. 2024, doi: 10.1080/13561820.2023.2204890.
[7] D. J. Damen, G. G. Schoonman, B. Maat, M. Habibović, E. Krahmer, and S. Pauws, “Patients Managing Their Medical Data in Personal Electronic Health Records: Scoping Review,” J Med Internet Res, vol. 24, no. 12, p. e37783, Dec. 2022, doi: 10.2196/37783.
[8] N. Akhtar, N. Khan, S. Qayyum, M. I. Qureshi, and S. S. Hishan, “Effi-cacy and pitfalls of digital technol-ogies in healthcare services: A sys-tematic review of two decades,” Front. Public Health, vol. 10, p. 869793, Sept. 2022, doi: 10.3389/fpubh.2022.869793.
[9] D. E. Detmer and A. Gettinger, “Es-sential Electronic Health Record Reforms for This Decade,” JAMA, vol. 329, no. 21, p. 1825, June 2023, doi: 10.1001/jama.2023.3961.
[10] S. V. Flowerday and C. Xenakis, “Security and Privacy in Distributed Health Care Environments,” Meth-ods Inf Med, vol. 61, no. 01/02, pp. 001–002, May 2022, doi: 10.1055/a-1768-2966.
[11] A. López Martínez, M. Gil Pérez, and A. Ruiz-Martínez, “A Compre-hensive Review of the State-of-the-Art on Security and Privacy Issues in Healthcare,” ACM Comput. Surv., vol. 55, no. 12, pp. 1–38, Dec. 2023, doi: 10.1145/3571156.
[12] M. Mahmood et al., “Improving Security Architecture of Internet of Medical Things: A Systematic Lit-erature Review,” IEEE Access, vol. 11, pp. 107725–107753, 2023, doi: 10.1109/ACCESS.2023.3281655.
[13] H. Guo, W. Li, M. Nejad, and C.-C. Shen, “A Hybrid Blockchain-Edge Architecture for Electronic Health Record Management With Attrib-ute-Based Cryptographic Mecha-nisms,” IEEE Trans. Netw. Serv. Manage., vol. 20, no. 2, pp. 1759–1774, June 2023, doi: 10.1109/TNSM.2022.3186006.
[14] L. D. Costa, B. Pinheiro, W. Cor-deiro, R. Araújo, and A. Abelém, “Sec-Health: A Blockchain-Based Protocol for Securing Health Rec-ords,” IEEE Access, vol. 11, pp. 16605–16620, 2023, doi: 10.1109/ACCESS.2023.3245046.
[15] P. Ruotsalainen and B. Blobel, “Transformed Health Ecosystems—Challenges for Security, Privacy, and Trust,” Front. Med., vol. 9, p. 827253, Mar. 2022, doi: 10.3389/fmed.2022.827253.
[16] A. Fischer, B. Fuhry, J. Kußmaul, J. Janneck, F. Kerschbaum, and E. Bodden, “Computation on Encrypt-ed Data Using Dataflow Authenti-cation,” ACM Trans. Priv. Secur., vol. 25, no. 3, pp. 1–36, Aug. 2022, doi: 10.1145/3513005.
[17] Z. Sun, D. Han, D. Li, X. Wang, C.-C. Chang, and Z. Wu, “A block-chain-based secure storage scheme for medical information,” J Wire-less Com Network, vol. 2022, no. 1, p. 40, Dec. 2022, doi: 10.1186/s13638-022-02122-6.
[18] S. Rana, M. R. H. Mondal, and J. Kamruzzaman, “RBFK cipher: a randomized butterfly architecture-based lightweight block cipher for IoT devices in the edge computing environment,” Cybersecurity, vol. 6, no. 1, p. 3, Feb. 2023, doi: 10.1186/s42400-022-00136-7.
[19] C. Silva, V. A. Cunha, J. P. Barraca, and R. L. Aguiar, “Analysis of the Cryptographic Algorithms in IoT Communications,” Inf Syst Front, vol. 26, no. 4, pp. 1243–1260, Aug. 2024, doi: 10.1007/s10796-023-10383-9.
[20] U. Gulen and S. Baktir, “Side-Channel Resistant 2048-Bit RSA Implementation for Wireless Sensor Networks and Internet of Things,” IEEE Access, vol. 11, pp. 39531–39543, 2023, doi: 10.1109/ACCESS.2023.3268642.
[21] Y. Sun, F. P.-W. Lo, and B. Lo, “Lightweight Internet of Things Device Authentication, Encryption, and Key Distribution Using End-to-End Neural Cryptosystems,” IEEE Internet Things J., vol. 9, no. 16, pp. 14978–14987, Aug. 2022, doi: 10.1109/JIOT.2021.3067036.
[22] G. Cassiers, L. Masure, C. Momin, T. Moos, and F.-X. Standaert, “Prime-Field Masking in Hardware and its Soundness against Low-Noise SCA Attacks,” TCHES, pp. 482–518, Mar. 2023, doi: 10.46586/tches.v2023.i2.482-518.
[23] A. Attkan and V. Ranga, “Cyber-physical security for IoT networks: a comprehensive review on tradi-tional, blockchain and artificial in-telligence based key-security,” Complex Intell. Syst., vol. 8, no. 4, pp. 3559–3591, Aug. 2022, doi: 10.1007/s40747-022-00667-z.
[24] A. Badr, “Instant-Hybrid Neural-Cryptography (IHNC) based on fast machine learning,” Neural Comput & Applic, vol. 34, no. 22, pp. 19953–19972, Nov. 2022, doi: 10.1007/s00521-022-07539-0.
[25] A. J. Hintaw, S. Manickam, S. Karuppayah, M. A. Aladaileh, M. F. Aboalmaaly, and S. U. A. Laghari, “A Robust Security Scheme Based on Enhanced Symmetric Algorithm for MQTT in the Internet of Things,” IEEE Access, vol. 11, pp. 43019–43040, 2023, doi: 10.1109/ACCESS.2023.3267718.
[26] M. Li and N. Zhang, “Trajectory-Based Authenticated Key Estab-lishment for Dynamic Internet of Things,” IEEE Access, vol. 10, pp. 111419–111448, 2022, doi: 10.1109/ACCESS.2022.3215688.
[27] C. A. Stevens et al., “Ensemble ma-chine learning methods in screening electronic health records: A scoping review,” DIGITAL HEALTH, vol. 9, p. 20552076231173225, Jan. 2023, doi: 10.1177/20552076231173225.
[28] M. Zaresefat and R. Derakhshani, “Revolutionizing Groundwater Management with Hybrid AI Mod-els: A Practical Review,” Water, vol. 15, no. 9, p. 1750, May 2023, doi: 10.3390/w15091750.
[29] A. Maier, H. Köstler, M. Heisig, P. Krauss, and S. H. Yang, “Known operator learning and hybrid ma-chine learning in medical imag-ing—a review of the past, the pre-sent, and the future,” Prog. Biomed. Eng., vol. 4, no. 2, p. 022002, Apr. 2022, doi: 10.1088/2516-1091/ac5b13.
[30] C. Yan et al., “A Multifaceted benchmarking of synthetic electron-ic health record generation mod-els,” Nat Commun, vol. 13, no. 1, p. 7609, Dec. 2022, doi: 10.1038/s41467-022-35295-1.
[31] B. Vasey et al., “Reporting guide-line for the early-stage clinical evaluation of decision support sys-tems driven by artificial intelli-gence: DECIDE-AI,” Nat Med, vol. 28, no. 5, pp. 924–933, May 2022, doi: 10.1038/s41591-022-01772-9.
[32] F. Xie et al., “Benchmarking emer-gency department prediction mod-els with machine learning and pub-lic electronic health records,” Sci Data, vol. 9, no. 1, p. 658, Oct. 2022, doi: 10.1038/s41597-022-01782-9.
[33] B. Dawadi, B. Adhikari, and D. Sri-vastava, “Deep Learning Tech-nique-Enabled Web Application Firewall for the Detection of Web Attacks,” Sensors, vol. 23, no. 4, p. 2073, Feb. 2023, doi: 10.3390/s23042073.
[34] K. Ntshabele, B. Isong, N. Gasela, and A. M. Abu-Mahfouz, “A Trust-ed Security Key Management Serv-er in LoRaWAN: Modelling and Analysis,” JSAN, vol. 11, no. 3, p. 52, Sept. 2022, doi: 10.3390/jsan11030052.
[35] H. K. M. Tanaka, “Cosmic coding and transfer storage (COS-MOCATS) for invincible key stor-age,” Sci Rep, vol. 13, no. 1, p. 8746, May 2023, doi: 10.1038/s41598-023-35325-y.
[36] J. Daemen and V. Rijmen, The De-sign of Rijndael. in Information Se-curity and Cryptography. Berlin, Heidelberg: Springer Berlin Hei-delberg, 2002. doi: 10.1007/978-3-662-04722-4.
[37] R. L. Rivest, A. Shamir, and L. Adleman, “A method for obtaining digital signatures and public-key cryptosystems,” Commun. ACM, vol. 21, no. 2, pp. 120–126, Feb. 1978, doi: 10.1145/359340.359342.
[38] J. R. Quinlan, “Induction of deci-sion trees,” Mach Learn, vol. 1, no. 1, pp. 81–106, Mar. 1986, doi: 10.1007/BF00116251.
[39] S. Li, K. Surineni, and N. Prab-hakaran, “Cyber-Attacks on Hospi-tal Systems: A Narrative Review,” The American Journal of Geriatric Psychiatry: Open Science, Educa-tion, and Practice, vol. 7, pp. 30–39, Sept. 2025, doi: 10.1016/j.osep.2025.03.002.