Last Update: 04/05/2026 at 2:50 PM EST
Homomorphic Encryption Secures AI Data
Coverage from DigitalToday, IEEE Spectrum, and others
Articles
3
Latest Article
03/10
Active Days
169
Executive Summary
Homomorphic encryption is moving into AI systems to protect prompts, call data, and model output without decryption
- LG Uplus and CryptoLab are testing homomorphic encryption for the ixi-O AI call agent and AI contact centre
- The approach lets data be computed while remaining encrypted, reducing exposure if systems are hacked
- LG Uplus said the method could search call keywords and analyze customer data without decryption
- The companies said the lattice-based scheme aligns with post-quantum cryptography goals
- Duality built a private LLM inference framework that encrypts prompts before sending them to a model
- Duality uses CKKS, functional bootstrapping, and hardware acceleration on GPUs and FPGAs to improve throughput
- Researchers also described a quantum homomorphic encryption scheme for private neural network training and inference
Quick Facts
- What: Test homomorphic encryption for private AI computation
- Where: South Korea and encrypted AI service environments
- Why: To protect sensitive data from hacking and leaks
- Who: LG Uplus, CryptoLab, Duality, and researchers
- When: Reported in March with ongoing prototype testing
Coverage Timeline: 169 Days
Featured Article
LG Uplus and CryptoLab implement homomorphic encryption to safeguard customer data in AI call centre in South Korea in March.
Additional Articles
⭐⭐⭐
Duality demonstrates privacy preserving LLM inference with fully homomorphic encryption to protect prompts and model responses in production oriented settings.
⭐️⭐️
Researchers demonstrate a practical quantum homomorphic encryption scheme for neural networks through simulations, enabling private training and inference on remote networks.