Confidential Computing or Cryptographic Computing?

Author:

Popa Raluca Ada1

Affiliation:

1. UC Berkeley

Abstract

Secure computation via MPC/homomorphic encryption versus hardware enclaves presents tradeoffs involving deployment, security, and performance. Regarding performance, it matters a lot which workload you have in mind. For simple workloads such as simple summations, low-degree polynomials, or simple machine-learning tasks, both approaches can be ready to use in practice, but for rich computations such as complex SQL analytics or training large machine-learning models, only the hardware enclave approach is at this moment practical enough for many real-world deployment scenarios.

Publisher

Association for Computing Machinery (ACM)

Reference22 articles.

1. Anjuna. 2022. Confidential computing pioneer Anjuna makes cloud safe enough for even government and defense agencies; https://www.anjuna.io/press/israels-ministry-of-defense-selects-anjuna-security-software-to-lockdown-sensitive-data-in-public-clouds.

2. Confidential Computing Consortium; https://confidentialcomputing.io.

3. Confidential Computing Summit 2024; https://www.confidentialcomputingsummit.com.

4. Snoopy

5. Delignat-Lavaud A. Fournet C. Vaswani K. Clebsch S. Riechert M. Costa M. Russinovich M. 2023. Why should I trust your code? acmqueue 21(4); https://queue.acm.org/detail.cfm?id=3623460.

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