Experimental Evaluation of Fully Homomorphic Encryption for Privacy-Preserving V2X Communication

A Maliciously Secure and Fully Decentralized Threshold FHE Scheme with Native RNS Acceleration

: 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.

A Survey on Privacy-Preserving Techniques for Cloud Data Processing Using Homomorphic Encryption and Federated Learning

This paper presents a structured review of privacy-preserving data processing techniques for cloud environments built on HE and FL, individually and in hybrid combination, and identifies promising directions for future research.

A Hierarchical Benchmarking Framework for Homomorphic Encryption-Based Aggregation and Validation Using Real Smart Meter Data

Homomorphic encryption (HE) enables computation on encrypted data and has emerged as a promising technology for privacy-preserving distributed analytics. However, the practical deployment of HE in large-scale hierarchical systems requires a thorough understanding of its computational overhead, scalability, and accuracy. This paper presents a generic hierarchical benchmarking framework for systematically evaluating homomorphic encryption schemes in multi-level aggregation environments. The framework supports configurable aggregation topologies, detailed operation-level profiling, and multiple encryption backends, enabling consistent and reproducible performance analysis across node-, cluster-, and global-level aggregation stages. Using the proposed framework, we conduct a comparative evaluation of the Brakerski/Fan–Vercauteren (BFV) and Cheon–Kim–Kim–Song (CKKS) schemes under identical workloads. Experimental results show that CKKS consistently outperforms BFV, achieving a 44.3% reduction in aggregation latency and a 24.6% reduction in decryption latency. For the tested encoding and parameter settings, CKKS delivers significantly lower numerical error, reducing the mean absolute error from 3.21×10−3 to 5.91×10−10. The proposed framework offers a reusable and extensible platform for evaluating emerging HE schemes and privacy-preserving analytics applications, thereby supporting future research and deployment of secure distributed data processing systems.

A Performance Driven Decision Framework for Hybrid Homomorphic Encryption

: 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.

Integrity Checking Mechanism for PrivacyPreserved Auditing of Cloud Shared-Data

A viable, privacy-friendly auditing framework of clouds which guarantees the end-toend encrypted verification without sacrificing the efficiency is presented.

LLMs AND DATA PRIVACY