Evaluating the Potential of In-Memory Processing to Accelerate Homomorphic Encryption

TL;DR

This paper investigates how in-memory processing (PIM) architectures can significantly reduce the memory overhead of homomorphic encryption (HE), enhancing its practicality for secure cloud computing.

Contribution

It evaluates PIM-based acceleration for HE polynomial operations and integrates PIM into two open-source HE libraries, providing practical insights and performance analysis.

Findings

PIM reduces HE memory overhead effectively.

Integration with HE libraries shows promising performance gains.

Provides guidelines for adopting PIM in HE applications.

Abstract

The widespread adoption of cloud-based solutions introduces privacy and security concerns. Techniques such as homomorphic encryption (HE) mitigate this problem by allowing computation over encrypted data without the need for decryption. However, the high computational and memory overhead associated with the underlying cryptographic operations has hindered the practicality of HE-based solutions. While a significant amount of research has focused on reducing computational overhead by utilizing hardware accelerators like GPUs and FPGAs, there has been relatively little emphasis on addressing HE memory overhead. Processing in-memory (PIM) presents a promising solution to this problem by bringing computation closer to data, thereby reducing the overhead resulting from processor-memory data movements. In this work, we evaluate the potential of a PIM architecture from UPMEM for accelerating HE operations. Firstly, we focus on PIM-based acceleration for polynomial operations, which underpin HE algorithms. Subsequently, we conduct a case study analysis by integrating PIM into two popular and open-source HE libraries, OpenFHE and HElib. Our study concludes with key findings and takeaways gained from the practical application of HE operations using PIM, providing valuable insights for those interested in adopting this technology.