This work proposes a watermarking technique for RLWE-based HE ciphertexts by exploiting the algebraic structure of RLWE polynomials and introduces two practical schemes that preserve the original security of HE while maintaining correctness and watermark robustness.
Abstract
Homomorphic encryption (HE) schemes based on the Ring Learning with Errors (RLWE) problem have been rapidly developed and widely applied to secure computation tasks, such as privacy-preserving deep learning inference and database queries. However, existing HE schemes mainly focus on the feasibility and efficiency of homomorphic computation, while practical requirements including ciphertext copyright protection, provenance tracking, and computation supervision remain largely unexplored. In this work, we propose a watermarking technique for RLWE-based HE ciphertexts. By exploiting the algebraic structure of RLWE polynomials, we embed watermark information into ciphertext noise without affecting plaintext correctness. To address watermark degradation caused by homomorphic operations, we introduce two practical schemes. The first scheme, ARWMark, leverages noise stratification to achieve robustness against homomorphic additive operations. The second scheme, MRWMark, is based on the roots of a linear equation and supports zero-bit watermarking while remaining robust against both homomorphic addition and multiplication. We provide rigorous theoretical analysis demonstrating that the proposed schemes preserve the original security of HE while maintaining correctness and watermark robustness. Extensive experiments further validate the effectiveness and practicality of the proposed watermarking schemes.
This work proposes a practical non-interactive encrypted retrieval framework for RAG based on threshold selection, and introduces a precision-stable mask polarization method that ensures accurate recovery of selected documents.
Yang Gao, Gang Quan, Scott Piersall et al.· arXiv.org· 0 citations
: Threshold fully homomorphic (ThFHE) encryption, as a communication encryption protocol, ensures that no third party participates in generating or knows any parameters. Compared to multi-key fully homomorphic encryption, it avoids excessive noise expansion caused by too many users participating in the calculation. However, current ThFHE algorithms focus on reducing computational overhead, thereby neglecting integrity verification of participating nodes’ behavior in distributed collaborative environments, leaving the system vulnerable to malicious actors. Without an effective verification mechanism, malicious nodes can manipulate the final result without breaking the protocol flow by injecting biased noise or providing forged partial decryption values, compromising data integrity. This research proposes an enhanced ThFHE encryption scheme based on a Full Remainder System (Full-RNS) architecture. This scheme integrates Distributed Key Generation (DKG) and Multi-Party Computation Relinearized Key (MPC RLK) techniques to achieve fully decentralized parameter initialization. To combat malicious attacks, we introduce a Non-Interactive Zero-Knowledge (NIZK) proof that incorporates smudging noise, ensuring that the computational trajectory at each stage can be publicly verified without leaking private key information. The results of the experiment show that this scheme maintains efficient homomorphic computation of the BFV algorithm while effectively resisting node fraud, providing more robust security for voting systems and medical privacy computations.
Ting-Yu Chen, Arijit Karati, Er-Shuo Zhuang et al.· International Conference on...· 0 citations
: To support privacy-preserving computation in resource-constrained environments, this paper presents a systematic performance comparison between homomorphic encryption (HE) and hybrid homomorphic encryption (HHE) on embedded edge hardware. While HE enables computations on encrypted data without decryption, its practical implementation remains challenging due to extensive computational and memory requirements. HHE alleviates this burden by having the client encrypt data with the lightweight symmetric cipher instead, while the data processor (server) handles the intensive transciphering into homomorphic format. We investigate the performance trade-offs between direct encryption with the homomorphic encryption scheme TFHE and a hybrid approach combining the Kreyvium stream cipher with TFHE. Testing on Raspberry Pi hardware shows HHE’s client-side advantages: Kreyvium’s pure encryption time is two times faster than TFHE, and it requires only 0.02% of its RAM. This efficiency comes at the cost of increased server-side processing during transciphering, shifting the computational burden from encryptors (clients) to data processors (servers). Based on measurements across different hardware configurations, we develop a decision framework for selecting HHE or HE based on data characteristics and operational requirements.
Hannah Meinhardt, Clemens Krüger, Dominik Schoop· International Conference on...· 0 citations
This work presents a framework that reformulates HE-aware model design as a constrained neural architecture search problem, where the objective is to identify architectures that are both cryptographically feasible and computationally efficient while preserving task performance.
Reeshav Chowdhury, Anoop Mishra, Deepak Khazanchi et al.· ACM Transactions on Internet...· 0 citations
This work identifies homomorphic multiplication as the most error-sensitive operation in practical HE pipelines and characterize how faults propagate and amplify through it, exposing a critical robustness vulnerability and motivating the need for more resilient HE deployments.
Matías Mazzanti, Vattana Chan, Karthik Swaminathan et al.· 0 citations
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