Privacy preservation has become a critical concern for governments, hospitals, and large corporations. Homomorphic encryption (HE) enables a ciphertext-based computation paradigm with strong security guarantees. In emerging cross-agency data cooperation scenarios like vertical federated learning (VFL), HE protects the data interaction from exposure to counterparts. However, computation on ciphertext has significant performance challenges due to increased data size and substantial overhead. Related work has been proposed to accelerate HE using parallel hardware, such as GPUs, FPGAs, and ASICs. However, many existing hardware accelerators target specific HE operations, such as number theoretic transform (NTT) and key switching, providing limited performance improvement for end-to-end applications. Others support bootstrapping, which requires quite a large ASIC design. To better support existing VFL training applications, we propose SAFE, an HE accelerator for scalable homomorphic matrix-vector products (HMVPs), which is the performance bottleneck. SAFE adopts a coefficient-wise encoded HMVP algorithm, despite a vanilla mode, we further explore the compressed and concatenated modes, which can fully utilize the polynomial encoding slots. The proposed hardware architecture, customized for HMVP dataflow, supports spatial and temporal parallelization of function units. The most costly polynomial function, NTT, is implemented with a low-area constant geometry unit which improves efficiency by$2.43\times $. SAFE is implemented as a CPU-FPGA heterogeneous acceleration system, unleashing the multithread potential. The evaluation demonstrates an up to$36\times $speed-up in end-to-end federated logistic regression training.

Homomorphic encryption (HE) is a promising technique for privacy-preserving computing because it allows computation on encrypted data without decryption. HE, however, suffers from poor performance due to enlarged data size and exploded amount of computation. Related work has been proposed to accelerate HE using GPUs, FPGAs, and ASICs. The existing work, however, aims at specific HE schemes and fails to consider the fast-evolving algorithms. For example, HE algorithms that combine different HE schemes have demonstrated capability of supporting more types of HE operations and ciphertexts. Moreover, some existing hardware accelerators target small HE operations (such as number theoretic transform and key-switch), which however provides limited or even neglected performance improvement for end-to-end applications. To better support existing privacy-preserving applications (e.g., logistic regression and neural network inference), we propose CHAM, an HE accelerator, for high-performance matrix-vector product, which can be easily extended to 2-D and 3-D convolutions. Motivated by the evolution of algorithms, CHAM supports not only traditional HE operations, but also different types of ciphertexts and the conversion between them. We implement CHAM with Xilinx FPGAs. The evaluation demonstrates 1800× speed-up for matrix-vector product, 36× speed-up for logistic regression, and 144× speed-up for Beaver triple generation compared to the existing work.

Confidentiality is an indispensable requirement in financial applications of blockchain technology, and supporting it along with high performance and friendly programmability is technically challenging. In this paper, we present a system design called CONFIDE to support on-chain confidentiality by leveraging Trust Execution Environment (TEE). CONFIDE's secure data transmission protocol and data encryption protocol, together with a highly efficient virtual machine run in TEE, guarantee the confidentiality in the life cycle of a transaction from end to end. CONFIDE proposes a secure data model along with an application-driven secure protocol to guarantee data confidentiality and integrity. Its smart contract language extension offers users the flexibility to define complex confidentiality models. CONFIDE is implemented as a plugin module to Antfin Blockchain's proprietary platform, and can be plugged into other blockchain platforms as well with its universal interface design. Nowadays, CONFIDE is supporting millions of commercial transactions daily on consortium blockchain running financial applications including supply chain finance, ABS, commodity provenance, and cold-chain logistics.