Evaluating the Potential of In-Memory Processing to Accelerate Homomorphic Encryption
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arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866916519884095488 |
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| author | Mwaisela, Mpoki Hari, Joel Yuhala, Peterson Ménétrey, Jämes Felber, Pascal Schiavoni, Valerio |
| author_facet | Mwaisela, Mpoki Hari, Joel Yuhala, Peterson Ménétrey, Jämes Felber, Pascal Schiavoni, Valerio |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_09144 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Evaluating the Potential of In-Memory Processing to Accelerate Homomorphic Encryption Mwaisela, Mpoki Hari, Joel Yuhala, Peterson Ménétrey, Jämes Felber, Pascal Schiavoni, Valerio Cryptography and Security Hardware Architecture Distributed, Parallel, and Cluster Computing Performance 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. |
| title | Evaluating the Potential of In-Memory Processing to Accelerate Homomorphic Encryption |
| topic | Cryptography and Security Hardware Architecture Distributed, Parallel, and Cluster Computing Performance |
| url | https://arxiv.org/abs/2412.09144 |