Nemesis: Noise-randomized Encryption with Modular Efficiency and Secure Integration in Machine Learning Systems
This work addresses the problem of making privacy-preserving ML systems more practical for large-scale applications, though it is incremental as it builds on prior caching techniques.
The paper tackles the computational inefficiency of Fully Homomorphic Encryption (FHE) in machine learning systems by proposing Nemesis, a framework that accelerates FHE-based operations without compromising accuracy or security, achieving significant reductions in computational overhead as demonstrated on datasets like MNIST, FashionMNIST, and CIFAR-10.
Machine learning (ML) systems that guarantee security and privacy often rely on Fully Homomorphic Encryption (FHE) as a cornerstone technique, enabling computations on encrypted data without exposing sensitive information. However, a critical limitation of FHE is its computational inefficiency, making it impractical for large-scale applications. In this work, we propose \textit{Nemesis}, a framework that accelerates FHE-based systems without compromising accuracy or security. The design of Nemesis is inspired by Rache (SIGMOD'23), which introduced a caching mechanism for encrypted integers and scalars. Nemesis extends this idea with more advanced caching techniques and mathematical tools, enabling efficient operations over multi-slot FHE schemes and overcoming Rache's limitations to support general plaintext structures. We formally prove the security of Nemesis under standard cryptographic assumptions and evaluate its performance extensively on widely used datasets, including MNIST, FashionMNIST, and CIFAR-10. Experimental results show that Nemesis significantly reduces the computational overhead of FHE-based ML systems, paving the way for broader adoption of privacy-preserving technologies.