Nemesis: Noise-randomized Encryption with Modular Efficiency and Secure Integration in Machine Learning Systems

• URL: http://arxiv.org/abs/2412.14392v1
• Date: Wed, 18 Dec 2024 22:52:12 GMT
• Title: Nemesis: Noise-randomized Encryption with Modular Efficiency and Secure Integration in Machine Learning Systems
• Authors: Dongfang Zhao,
• Abstract summary: Nemesis is a framework that accelerates FHE-based machine learning systems without compromising accuracy or security. We prove the security of Nemesis under standard cryptographic assumptions. Results show that Nemesis significantly reduces the computational overhead of FHE-based ML systems.
• Score: 1.3824176915623292
• License:
• Abstract: 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.

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