This paper enhances Federated Learning privacy by integrating two key protection mechanisms—masking and RSA encapsulation—into Hybrid Homomorphic Encryption (HHE) to secure against malicious clients.

This paper provides a comprehensive, system-level comparison of MPC and FHE for Privacy-Preserving Machine Learning (PPML) across various models and environments, moving beyond single-metric latency a…

The paper introduces public-decay Homomorphic State Space Models (HSSMs) that enable efficient, high-accuracy sequence inference directly on encrypted data, significantly outperforming existing encryp…

This paper evaluates the vulnerability of Fully Homomorphic Encryption (FHE) computation to silent data corruption (SDC) using large-scale fault-injection experiments and theoretical analysis.

This paper provides a comparative analysis and benchmarking of Secure Multi-Party Computation (SMPC) and Fully Homomorphic Encryption (FHE) for machine learning, finding that the optimal choice depend…

The paper proposes and validates a privacy-preserving framework using Homomorphic Encryption (HE) to train and run Machine Learning models on sensitive data while keeping it encrypted throughout the e…

The paper provides the first theoretical convergence analysis for machine learning training under fully homomorphic encryption combined with differential privacy, improving efficiency and scalability.

The paper proposes a co-design paradigm, 'Meeting in the Middle,' to make Fully Homomorphic Encryption (FHE) practical for AI inference by optimizing both the cryptographic schemes and the underlying…

EncFormer is a novel two-party framework that significantly improves the efficiency and scalability of private Transformer inference by optimizing the combination of Fully Homomorphic Encryption (FHE)…

The paper proposes Independent Vector Evaluation (IVE), a novel method that significantly reduces the computational cost of generating selection vectors for private embedding lookups under Fully Homom…

The paper proposes a novel space switching method to efficiently unify arithmetic and comparison operations within Fully Homomorphic Encryption (FHE) schemes, achieving significant performance improve…

This paper develops optimized algorithms and a pipeline architecture for high-throughput, memory-efficient batch processing of encrypted neural network inference, significantly improving performance o…

The paper proposes a novel symmetric Fully Homomorphic Encryption (FHE) scheme that manages noise growth and computational overhead by fragmenting the plaintext and using a dual-regulator system for m…

The paper proposes a novel four-phase protocol to enable secure, multi-key homomorphic encryption (xMK-CKKS) aggregation for zero-order Federated Learning over wireless channels without requiring chan…

The paper proposes a scalable, privacy-preserving framework for iris recognition using Fully Homomorphic Encryption (FHE), achieving accuracy comparable to cleartext while identifying the computationa…

The paper introduces a unified framework for Quantum Fully Homomorphic Encryption (QFHE) that achieves exponential efficiency improvements by integrating a novel modular arithmetic program (MAP) tailo…

The paper introduces a framework, PD-FHC, that allows users to outsource Boolean computations to an untrusted cloud while guaranteeing both computational privacy and plausible deniability against coer…

The paper proposes a kernel-based, polynomial approximation of the ReLU activation function to enable the use of non-linear deep learning models, such as LLMs, within the constraints of Homomorphic En…

The paper demonstrates that standard homomorphic encryption (HE) schemes are insufficient to guarantee integrity in networked control systems (NCS) against covert attacks, proposing instead a verifiab…

The paper proposes $HE^2$, a novel communication-light heterogeneous accelerator architecture that significantly improves the efficiency of Fully Homomorphic Encryption (FHE) by optimizing dataflow an…