A Hybrid Approach to Secure Function Evaluation Using SGX
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Abstract (arXiv)
A protocol for two-party secure function evaluation (2P-SFE) aims to allow the parties to learn the output of function f of their private inputs, while leaking nothing more. In a sense, such a protocol realizes a trusted oracle that computes f and returns the result to both parties. There have been tremendous strides in efficiency over the past ten years, yet 2P-SFE protocols remain impractical for most real-time, online computations, particularly on modestly provisioned devices. Intel's Software Guard Extensions (SGX) provides hardware-protected execution environments, called enclaves, that may be viewed as trusted computation oracles. While SGX provides native CPU speed for secure computation, previous side-channel and micro-architecture attacks have demonstrated how security guarantees of enclaves can be compromised. In this paper, we explore a balanced approach to 2P-SFE on SGX-enabled processors by constructing a protocol for evaluating f relative to a partitioning of f. This approach alleviates the burden of trust on the enclave by allowing the protocol designer to choose which components should be evaluated within the enclave, and which via standard cryptographic techniques. We describe SGX-enabled SFE protocols (modeling the enclave as an oracle), and formalize the strongest-possible notion of 2P-SFE for our setting. We prove our protocol meets this notion when properly realized. We implement the protocol and apply it to two practical problems: privacy-preserving queries to a database, and a version of Dijkstra's algorithm for privacy-preserving navigation. Our evaluation shows that our SGX-enabled SFE scheme enjoys a 38x increase in performance over garbled-circuit-based SFE. Finally, we justify modeling of the enclave as an oracle by implementing protections against known side-channels.
Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). First posted on arXiv 3 May 2019.
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Citing papers per year: 2022: 2; 2023: 2; 2024: 1; 2025: 1.
6 papers.
- 2023AutoReP: Automatic ReLU Replacement for Fast Private Network Inferencecited by 29
- 2022Aggregation Service for Federated Learning: An Efficient, Secure, and More Resilient Realizationcited by 16
- 2023A Survey of Secure Computation Using Trusted Execution Environmentscited by 8
- 2022PolyMPCNet: Towards ReLU-free Neural Architecture Search in Two-party Computation Based Private Inferencecited by 2
- 2024Privacy-Preserving and Trustworthy Deep Learning for Medical Imagingcited by 0
- 2025DeepShare: Sharing ReLU Across Channels and Layers for Efficient Private Inferencecited by 0
- 2025DeepShare: Sharing ReLU Across Channels and Layers for Efficient Private Inference19 Dec 2025
- 2024Privacy-Preserving and Trustworthy Deep Learning for Medical Imaging29 Jun 2024
- 2023AutoReP: Automatic ReLU Replacement for Fast Private Network Inference20 Aug 2023
- 2023A Survey of Secure Computation Using Trusted Execution Environments23 Feb 2023
- 2022PolyMPCNet: Towards ReLU-free Neural Architecture Search in Two-party Computation Based Private Inference20 Sep 2022
- 2022Aggregation Service for Federated Learning: An Efficient, Secure, and More Resilient Realization4 Feb 2022