RAMSES is a new HPC system that integrates hardware-based memory encryption and state-of-the-art file encryption to deliver high performance and robust security.

This paper proposes a decentralized, confidential computing platform using Intel TDX, ITA, and NVIDIA CC for secure and affordable AI workloads.

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 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…

C8s is a confidential computing architecture for Kubernetes that uses hardware Trusted Execution Environments (TEEs) to provide cryptographically provable confidentiality, integrity, and verifiability…

The paper introduces BSGS-Diagonal, a memory-efficient algorithm, and GPU-optimized kernels to significantly accelerate and reduce the resource overhead of encrypted face recognition using Fully Homom…

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…

This paper proposes and demonstrates a method to secure high-performance RDMA data transfers by implementing AES-128 encryption directly within a programmable network switch, maintaining high throughp…

This paper presents an efficient fault-tolerance scheme for CPU-based fully homomorphic encryption (FHE) that reduces protection overhead and achieves 100 percent detection rate under random single-bi…

This paper conducts an extensive microbenchmark study to characterize the performance of core cryptographic workloads across various cloud services, architectures, and programming languages, identifyi…

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…

This paper presents SCP, a cache partitioning design that combines strict eviction isolation with write-shared coherence to mitigate eviction-based cache side channels.

This paper proposes methods to optimally permute the rows and columns of a sparse matrix to minimize the number of cyclic diagonals required for homomorphic sparse-matrix vector multiplication, signif…

The paper introduces EBCC, an OCI-compatible runtime architecture that manages composite confidential-computing workloads by integrating TEE-backed execution into the standard container lifecycle.

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.

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 introduces 'bridges,' a type of morphism between encryption schemes, and provides a general recipe for constructing them, showing that their security relies on the security of the initial sc…

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 introduces HPCCFA, a novel mechanism that leverages Hardware Performance Counters (HPCs) to provide hardware-backed Control Flow Attestation (CFA) on commodity CPUs, thereby enhancing the se…