IRON: Functional Encryption using Intel SGX
By Ben Fisch, Dhinakaran Vinayagamurthy, Dan Boneh, Sergey Gorbunov. 2017
171 citations recorded by the source corpus.
Subfields: Artificial Intelligence
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Florian Tramèr
- Yang Zhang
- Dawn Song
- Dan Boneh
- Mario Fritz
- Michael Backes
- Nicholas Carlini
- Nicolas Papernot
Citations in the corpus
- Slalom: Fast, Verifiable and Private Execution of Neural Networks in Trusted Hardware (2018, background): “We refer the interested reader to (Pass et al., 2017; Fisch et al., 2017; Subramanyan et al., 2017) for different attempts at such formalisms.”
- Ekiden: A Platform for Confidentiality-Preserving, Trustworthy, and Performant Smart Contracts (2018, positive): “TEEbased computation in Ekiden provides confidentiality, enabling efficient use of powerful cryptographic primitives that a TEE is known to emulate, such as functional encryption [29] and black-box obfuscation [58], and also provides a trustworthy source of randomness, a major acknowledged difficult”
- hPRESS: A Hardware-Enhanced Proxy Re-Encryption Scheme Using Secure Enclave (2020, methodology): “Following the system model of Iron in [6], we define a secure hardware model HW for probabilistic-polynomial-time programs Q consisting of the following interface: HW .”
- The Ekiden Platform for Confidentiality-Preserving, Trustworthy, and Performant Smart Contracts (2020, background): “…in Ekiden provides confidentiality, enabling efficient use of powerful cryptographic primitives that a TEE is known to emulate, such as functional encryption 9 and black-box obfus-cation, 12 and also provides a trustworthy source of randomness, a major acknowledged difficulty in blockchain systems.”
- Tesseract: Real-Time Cryptocurrency Exchange using Trusted Hardware (2017, methodology): “Trusted hardware can also be used to achieve significant efficiency gains for well-known cryptographic primitives such as functional encryption [34], secure MPC [71], and NIZK in the presence of side-channel attacks [83].”
- MLCapsule: Guarded Offline Deployment of Machine Learning as a Service (2018, background): “[10] can be used as a building block of MLCapsule.”