Private information retrieval (PIR) is an essential cryptographic protocol for privacy-preserving applications, enabling a client to retrieve a record from a server's database without revealing which record was requested. Single-server PIR based on homomorphic encryption has particularly gained immense attention for its ease of deployment and reduced trust assumptions. However, single-server PIR remains impractical due to its high computational and memory bandwidth demands. Specifically, reading the entirety of large databases from storage, such as SSDs, severely limits its performance. To address this, we propose IVE, an accelerator for single-server PIR with a systematic extension that enables practical retrieval from large databases using DRAM. Recent advances in DRAM capacity allow PIR for large databases to be served entirely from DRAM, removing its dependence on storage bandwidth. Although the memory bandwidth bottleneck still remains, multi-client batching effectively amortizes database access costs across concurrent requests to improve throughput. However, client-specific data remains a bottleneck, whose bandwidth requirements ultimately limits performance. IVE overcomes this by employing a large on-chip scratchpad with an operation scheduling algorithm that maximizes data reuse, further boosting throughput. Additionally, we introduce sysNTTU, a versatile functional unit that enhances area efficiency without sacrificing performance. We also propose a heterogeneous memory system architecture, which enables a linear scaling of database sizes without a throughput degradation. Consequently, IVE achieves up to$1,275 \times$higher throughput compared to prior PIR hardware solutions.

Fully homomorphic encryption (FHE) is a promising solution for privacy-preserving cloud computing as it enables computations on sensitive data without any risk of data leakage. Despite the significant attention FHE has received, substantial computation and memory demands make it hardly practical for real-world applications. We propose a readily available and practical hardware solution to tackle this problem by using GPUs. GPUs have adequate computational and memory resources to handle complex operations in FHE, including number-theoretic transform (NTT), on which most prior work has deeply focused. However, through detailed analyses, we discover that the performance bottleneck on GPUs is primarily due to simpler element-wise operations, which are limited by off-chip memory (DRAM) bandwidth. Motivated by these observations, we develop Anaheim, a processing-in-memory (PIM) architecture for FHE. We develop optimized FHE execution flows and an end-toend software framework for using PIM with GPUs. Also, we design a versatile PIM unit that handles various modular integer arithmetic PIM instructions, along with an efficient data mapping and associated PIM execution algorithms that minimize data access overhead by leveraging the internal structure of DRAM. Our concerted efforts substantially enhance the performance and energy efficiency of various FHE workloads on GPUs.

Fully homomorphic encryption (FHE) is an emerging cryptographic technology that guarantees the privacy of sensitive user data by enabling direct computations on encrypted data. Despite the security benefits of this approach, FHE is associated with prohibitively high levels of computational and memory overhead, preventing its widespread use in real-world services. Numerous domain-specific hardware designs have been proposed to address this issue, but most of them use excessive amounts of chip area and power, leaving room for further improvements in terms of practicality.

Homomorphic encryption (HE) enables the secure offloading of computations to the cloud by providing computation on encrypted data (ciphertexts). HE is based on noisy encryption schemes in which noise accumulates as more computations are applied to the data. The limited number of operations applicable to the data prevents practical applications from exploiting HE. Bootstrapping enables an unlimited number of operations or fully HE (FHE) by refreshing the ciphertext. Unfortunately, bootstrapping requires a significant amount of additional computation and memory bandwidth as well. Prior works have proposed hardware accelerators for computation primitives of FHE. However, to the best of our knowledge, this is the first to propose a hardware FHE accelerator that supports bootstrapping as a first-class citizen.

Homomorphic Encryption (HE) is one of the most promising post-quantum cryptographic schemes that enable privacy-preserving computation on servers. However, noise accumulates as we perform operations on HE-encrypted data, restricting the number of possible operations. Fully HE (FHE) removes this restriction by introducing the bootstrapping operation, which refreshes the data; however, FHE schemes are highly memory-bound. Bootstrapping, in particular, requires loading GBs of evaluation keys and plaintexts from offchip memory, which makes FHE acceleration fundamentally bottlenecked by the off-chip memory bandwidth.In this paper, we propose ARK, an Accelerator for FHE with Runtime data generation and inter-operation Key reuse. ARK enables practical FHE workloads with a novel algorithm-architecture co-design to accelerate bootstrapping. We first eliminate the off-chip memory bandwidth bottleneck through runtime data generation and inter-operation key reuse. This approach enables ARK to fully exploit on-chip memory by substantially reducing the size of the working set. On top of such algorithmic enhancements, we build ARK microarchitecture that minimizes on-chip data movement through an efficient, alternating data distribution policy based on the data access patterns and a streamlined dataflow organization of the tailored functional units – including base conversion, number-theoretic transform, and automorphism units. Overall, our codesign effectively handles the heavy computation and data movement overheads of FHE, drastically reducing the cost of HE operations, including bootstrapping.

Homomorphic Encryption (HE) [11] draws significant attention as a privacy-preserving way for cloud computing because it allows computation on encrypted messages called ciphertexts. Among numerous FHE schemes [2]–[4], [8], [9], HE for Arithmetic of Approximate Numbers (HEAAN [3]), which is also known as CKKS (Cheon-Kim-Kim-Song), is rapidly gaining popularity [10] as it supports computation on real numbers. A critical shortcoming of HE is the high computational complexity of ciphertext arithmetic, especially, HE multiplication (HE Mul). For example, the execution time for computation on encrypted data (ciphertext) increases from 100s to 10,000s of times compared to that on native, unen-crypted messages. However, a large body of HE acceleration studies, including ones exploiting GPUs and FPGAs, lack a rigorous analysis of computational complexity and data access patterns of HE Mul with large parameter sets on CPUs, the most popular computing platform.