Systematic Literature Review: Cryptographic Protocols for Securing Genomic Data and Privacy
DOI:
https://doi.org/10.53089/medula.v15i4.1840Keywords:
Cryptography, Genomic Data, Data Privacy, Homomorphic Encryption, CybersecurityAbstract
precision medicine. However, the static and hereditary nature of genomic data poses significant privacy risks for individuals and their families. This article aims to conduct a systematic literature review of various modern cryptographic protocols designed to protect genomic data. The research method utilizes a Systematic Literature Review (SLR) approach, analyzing relevant literature from 2018 to 2026. The findings indicate that protocols such as Homomorphic Encryption (HE) and Secure Multi-party Computation (SMPC) offer robust protection but face challenges regarding computational efficiency and bandwidth. Differential Privacy (DP) provides an efficient alternative but carries risks of reduced data accuracy. In conclusion, securing genomic data requires hybrid models that integrate algorithmic efficiency with long-term resilience, particularly against quantum computing threats. Aligning these technologies with Indonesia's Personal Data Protection Law (UU PDP) is crucial for building a secure and credible national health data ecosystem.
References
Wang S, Zhang Y, Tang H. Privacy-preserving genomic data analysis: a systematic review. Brief Bioinform. 2023;24(1):bbac521.
Naveed M, Ayday E, Clayton EW, et al. Privacy in the genomic era. ACM Comput Surv. 2015;48(1):1–44.
Rivest RL, Shamir A, Adleman L. Advanced cryptography in health information systems: a review of cryptographic protocols. IEEE Access. 2022;10:45210–45225.
Erlich Y, Narayanan A. Routes for breaching and protecting genetic privacy. Nat Rev Genet. 2014;15(6):383–397.
Mittelstadt BD, Floridi L. The ethics of big data in health care: a systematic review. Sci Eng Ethics. 2016;22(2):303–341.
Republik Indonesia. Undang-Undang Republik Indonesia Nomor 27 Tahun 2022 tentang Pelindungan Data Pribadi. Lembaran Negara Republik Indonesia Tahun 2022 Nomor 196. Jakarta; 2022.
Kementerian Kesehatan Republik Indonesia. Biomedical & Genome Science Initiative (BGSi): Strategi Transformasi Digital Kesehatan Nasional. Jakarta: Kemenkes RI; 2024.
Raisaro JL, Troncoso-Pastoriza JR, et al. Computing with data privacy: steps towards realizing the clinical promise of genomics. Nat Med. 2019;25(9):1320–1322.
Gentry C. A fully homomorphic encryption scheme for big data analysis. J Cryptol. 2021;34(3):1–25.
Bater C, Rogers G, Carter A, Egert C. SMCQL: secure querying for federated genomic databases. Proc VLDB Endow. 2020;13(11):2345–2358.
Dwork C, Roth A. The algorithmic foundations of differential privacy. Found Trends Theor Comput Sci. 2019;9(3–4):211–407.
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