A Hybrid Approach to Secure Function Evaluation using SGX

Abstract

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 SGXenabled 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 SGXenabled 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. CCS CONCEPTS• Security and privacy → Formal security models; Privacypreserving protocols; Hardware-based security protocols.

CrypTFlow: Secure TensorFlow Inference

“…(a) The threat model of Aramis is significantly stronger than the prior work on MPC using secure hardware [74], [69], [38], [11], [23], [29], [51], [32], [77], [83], [44]. Specifically, in our threat model, not only is the host operating system outside the Trusted Computing Base, but it is also allowed to observe the entire state of the hardware (including user data).…”

OblivSketch: Oblivious Network Measurement as a Cloud Service

“…Recent studies [1], [19], [25], [29], [30], [53], [59] tend to design data-oblivious systems to mitigate the memory access pattern leakage. Specifically, those systems adopt oblivious data structures (e.g., ORAM [63]) and algorithms to hide the access pattern on data as well as the code, which are the core sources exploited by the above attacks.…”

“…A wide range of applications have been implemented via oblivious systems, such as file system [1], database [29], collaborative analytics [25], and indexing system [53], [59]. Recent works propose generic programming frameworks [19], [30] that can compile any algorithm as an oblivious circuit running in the enclave. However, all the above systems are not designed and optimised for network measurements.…”

Practical Verifiable Computation by Using a Hardware-Based Correct Execution Environment

“…There are, however, approaches that employ SGX for VC of a specific application domain. Specifically, the SGX scheme has been used to accelerate VC for smart contracts [69], machine learning [70], secure function evaluation [71], and MapReduce [72], [73]. Verifiable ASICs [74] employ a hardware circuit to verify the untrusted hardware modules.…”