Understanding the Error Sensitivity of Privacy-Aware Computing

2025-06-09

AI Summary

This work identifies the vulnerability of homomorphic encryption (HE), particularly the CKKS scheme, to silent data corruption (SDC) under hardware- or software-induced bit errors. It presents the first quantitative analysis of CKKS robustness against single-bit flips, revealing a strong coupling between its inherent noise management and fault propagation dynamics. To characterize this behavior, we propose a hierarchical error-sensitivity model grounded in the Residue Number System (RNS) and optimized via the Number-Theoretic Transform (NTT), systematically modeling error injection and propagation pathways across CKKS operations. Empirical evaluation across core computational modules yields a precise sensitivity ranking and pinpoints critical vulnerability points most likely to induce SDC. Our findings provide the first empirical evidence and theoretical framework to guide fault-tolerant HE hardware design, error-resilient algorithm development, and reliability assurance for privacy-preserving computing systems.

Technology Category

• Computer Vision: Adversarial Attacks & Robustness
• Machine Learning: Hardware-aware ML
• Constraint Satisfaction and Optimization: Satisfiability Modulo Theories

Application Category

• Security and Privacy: Large-scale security measurements
• Semantics and Knowledge: Provenance, trust, security and privacy, and ethical issues in managing semantic data
• Responsible Web: Measurement, analysis, and circumvention of Web censorship

Abstract

Homomorphic Encryption (HE) enables secure computation on encrypted data without decryption, allowing a great opportunity for privacy-preserving computation. In particular, domains such as healthcare, finance, and government, where data privacy and security are of utmost importance, can benefit from HE by enabling third-party computation and services on sensitive data. In other words, HE constitutes the"Holy Grail"of cryptography: data remains encrypted all the time, being protected while in use. HE's security guarantees rely on noise added to data to make relatively simple problems computationally intractable. This error-centric intrinsic HE mechanism generates new challenges related to the fault tolerance and robustness of HE itself: hardware- and software-induced errors during HE operation can easily evade traditional error detection and correction mechanisms, resulting in silent data corruption (SDC). In this work, we motivate a thorough discussion regarding the sensitivity of HE applications to bit faults and provide a detailed error characterization study of CKKS (Cheon-Kim-Kim-Song). This is one of the most popular HE schemes due to its fixed-point arithmetic support for AI and machine learning applications. We also delve into the impact of the residue number system (RNS) and the number theoretic transform (NTT), two widely adopted HE optimization techniques, on CKKS' error sensitivity. To the best of our knowledge, this is the first work that looks into the robustness and error sensitivity of homomorphic encryption and, as such, it can pave the way for critical future work in this area.

Problem

• Analyzes error sensitivity in Homomorphic Encryption (HE) applications
• Investigates impact of RNS and NTT on CKKS error tolerance
• Addresses silent data corruption risks in privacy-preserving computations

Innovation

• Homomorphic Encryption enables secure computation on encrypted data
• Error sensitivity study of CKKS scheme in HE
• Impact of RNS and NTT on CKKS error sensitivity