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…

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 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…

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 TIGER, a GPU-accelerated framework that significantly speeds up high-precision evaluation of nonlinear layers for encrypted LLM inference using TFHE.

The paper characterizes Homomorphic Encryption (HE) operations on a real-world Processing-In-Memory (PIM) system, demonstrating that while PIM is a viable alternative to CPUs/GPUs, performance is limi…

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…

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 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…

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…

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…

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…

The paper introduces FHE-DiCSNN, a novel framework that uses the TFHE scheme to enable secure and efficient computation on Spiking Neural Networks (SNNs), achieving high accuracy and fast inference ti…

AEGIS is a novel system that significantly improves the scalability of running large, long-sequence Transformer models under Fully Homomorphic Encryption (FHE) on multi-GPU systems by optimizing data…

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 addresses the vulnerability of CKKS-based Fully Homomorphic Encryption (FHE) to overflow attacks by proposing a formal verification technique that guarantees certified bounds on all neuron r…

The paper proposes a novel, optimized sparse matrix multiplication method for fully homomorphic encrypted deep neural networks, achieving up to a 3.0x speedup on AMD GPUs compared to CPU implementatio…

The paper proposes a novel triple-hoisted baby-step giant-step algorithm and a memory-optimized FPGA accelerator to significantly reduce the ciphertext rotations and off-chip memory access latency whe…

The paper proposes a novel method using fully homomorphic encryption (FHE) to learn causal structures while preserving data privacy, achieving high consistency and practical efficiency.