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.

翻译：基于云计算的解决方案的广泛采用引发了隐私和安全方面的担忧。同态加密（HE）等技术通过允许在加密数据上进行计算而无需解密，从而缓解了这一问题。然而，与底层密码学操作相关的高计算和内存开销阻碍了基于HE的解决方案的实际应用。尽管大量研究集中于利用GPU和FPGA等硬件加速器来降低计算开销，但相对较少关注解决HE的内存开销问题。内存处理（PIM）通过使计算更接近数据，从而减少处理器与内存之间数据移动带来的开销，为此问题提供了一个有前景的解决方案。在本工作中，我们评估了UPMEM的一种PIM架构在加速HE操作方面的潜力。首先，我们专注于为支撑HE算法的多项式操作提供基于PIM的加速。随后，我们通过将PIM集成到两个流行且开源的HE库（OpenFHE和HElib）中进行了案例研究分析。我们的研究总结了从使用PIM进行HE操作的实际应用中获得的**关键发现和要点**，为有意采用该技术的研究者提供了有价值的见解。