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| Format: | Preprint |
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2025
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| Online Access: | https://arxiv.org/abs/2501.17292 |
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| _version_ | 1866915253611134976 |
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| author | Ghinani, Sahar Ghoflsaz Zhang, Jingyao Sadredini, Elaheh |
| author_facet | Ghinani, Sahar Ghoflsaz Zhang, Jingyao Sadredini, Elaheh |
| contents | 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 $14.66\times$ speedup compared to a secure CPU configuration while maintaining data confidentiality and integrity when outsourcing linear and/or nonlinear computation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_17292 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Enabling Low-Cost Secure Computing on Untrusted In-Memory Architectures Ghinani, Sahar Ghoflsaz Zhang, Jingyao Sadredini, Elaheh Cryptography and Security 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 $14.66\times$ speedup compared to a secure CPU configuration while maintaining data confidentiality and integrity when outsourcing linear and/or nonlinear computation. |
| title | Enabling Low-Cost Secure Computing on Untrusted In-Memory Architectures |
| topic | Cryptography and Security |
| url | https://arxiv.org/abs/2501.17292 |