# Enabling Low-Cost Secure Computing on Untrusted In-Memory Architectures

arXiv: https://arxiv.org/abs/2501.17292 · HTML: https://mlsanity.com/paper/2501.17292/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Jan 2025.

## Built on by (most cited first, 1 of 1)

- [PIMbot: A Self-Adaptive Attack Framework for Adversarial Manipulation of Multi-Robot Reinforcement Learning](https://mlsanity.com/paper/2605.23027/) (2026)

## Abstract (arXiv)

Modern computing systems are limited in performance by the memory bandwidth available to processors, a problem known as the memory wall. Processing-in-Memory (PIM) promises to substantially improve this problem by moving processing closer to the data, improving effective data bandwidth, and leading to superior performance on memory-intensive workloads. However, integrating PIM modules within a secure computing system raises an interesting challenge: unencrypted data has to move off-chip to the PIM, exposing the data to attackers and breaking assumptions on Trusted Computing Bases (TCBs). To tackle this challenge, this paper leverages multi-party computation (MPC) techniques, specifically arithmetic secret sharing and Yao's garbled circuits, to outsource bandwidth-intensive computation securely to PIM. Additionally, we leverage precomputation optimization to prevent the CPU's portion of the MPC from becoming a bottleneck. We evaluate our approach using the UPMEM PIM system over various applications such as Deep Learning Recommendation Model inference and Logistic Regression. Our evaluations demonstrate up to a 14.66× speedup compared to a secure CPU configuration while maintaining data confidentiality and integrity when outsourcing linear and/or nonlinear computation.