This paper investigates the preservation of data privacy generated by Internet of Things (IoT) devices during processing within artificial intelligence (AI) environments. To ensure the security of IoT data, homomorphic encryption (HE) algorithms (PHE, SHE, FHE) are analyzed. The mechanisms for executing mathematical operations on encrypted data within AI models (using linear regression as an example) are studied using Paillier, BFV, and CKKS schemes. The models are compared based on accuracy, computational latency, and memory overhead, and the most efficient algorithm is selected.
| Mualliflar | Saparniyazov, Berdax Abdirazakovich, Saymanov, Islambek Mirzabaevich, Bazarbaev, Axmet Qidirbayevich, Shaniyazova, Nesibeli Userbayevna, Saparniyazov, Berdax Abdirazakovich, Saymanov, Islambek Mirzabaevich, Bazarbaev, Axmet Qidirbayevich, Shaniyazova, Nesibeli Userbayevna, Сапарниязов, Бердах Абдиразакович, Сайманов, Исламбек Мирзабаевич, Базарбаев, Ахмет Кыдырбаевич, Шаниязова, Несибели Усербаевна |
|---|---|
| Jurnal | Ilim ha’m ja’miyet |
| Nashr sanasi | 2026-08-15 |
| Son | 5-1 |
| Betlar | 19-21 |
| Til | kaa |
IoT, sun’iy intellekt, gomomorfik shifrlash, Paillier, BFV, CKKS, maxfiylik, kiberxavfsizlik, chiziqli regressiya, IoT, искусственный интеллект, гомоморфное шифрование, Paillier, BFV, CKKS, конфиденциальность, кибербезопасность, линейная регрессия, IoT, аrtificial intelligence, homomorphic encryption, Paillier, BFV, CKKS, privacy, cybersecurity, linear regression
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