Optimasi Penggunaan Enkripsi Homomorfik untuk Keamanan dan Privasi Data Kesehatan (Studi Literatur)
DOI:
https://doi.org/10.70309/ticom.v13i2.139Keywords:
Enkripsi homomorfik, Privasi data, Federated Learning, IoMT, BlockchainAbstract
Digitalisasi dalam sektor kesehatan menghadirkan tantangan baru terkait privasi dan keamanan data medis. Artikel ini mengulas optimalisasi penggunaan enkripsi homomorfik (HE) untuk melindungi data kesehatan dalam kerangka Federated Learning (FL) dan Internet of Medical Things (IoMT). Kajian ini memaparkan tantangan implementasi HE, seperti konsumsi daya tinggi dan keterbatasan perangkat IoT, serta solusi untuk mengatasi kendala tersebut. Hasil tinjauan literatur menunjukkan bahwa inovasi algoritma HE hemat daya dan integrasi dengan teknologi blockchain mampu meningkatkan efisiensi dan keamanan dalam sistem kesehatan berbasis IoT. Studi ini menekankan pentingnya pengembangan teknologi enkripsi yang efisien guna mendukung interoperabilitas data medis dan meningkatkan kepercayaan publik terhadap sistem kesehatan digital
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