Secure Multiparty Computation from SGX

By Raad Bahmani, Manuel Barbosa, Ferdinand Brasser, Bernardo Portela, Ahmad-Reza Sadeghi, Guillaume Scerri, Bogdan Warinschi. 2017

104 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems

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Citations in the corpus

Federated Machine Learning (2019, background): “A trusted third party C a reasonable assumption since party C can be played by authorities such as governments or replaced by secure computing node such as Intel Software Guard Extensions (SGX) [7].”
Chameleon: A Hybrid Secure Computation Framework for Machine Learning Applications (2018, methodology): “The STP can be a separate computing node or it can be implemented based on a smartcard [33] or Intel Software Guard Extensions (SGX [7]).”
Shredder: Learning Noise Distributions to Protect Inference Privacy (2019, unknown): “Securemultipartycomputationreferstoagroup ofprotocolsthatenablemultiplepartiestojointlycomputea functionwhileeachpartysolelyhasaccesstoitsownpartof theinput[17,77].”
A Pragmatic Introduction to Secure Multi-Party Computation (2019, methodology): “, 2016; Bahmani et al., 2017; Zheng et al., 2017). This offers a different kind of a trade-off: should we trust the manufacturer of the hardware (we must consider both competence and possible prior or future malicious intent) to greatly improve performance of secure computing? There is no clear answ”
CrypTFlow: Secure TensorFlow Inference (2019, methodology): “Prior works that use hardware enclaves for secure computation [56], [52], [29], [9], [18] assume that the enclave hides all data residing in it from the host.”
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].”
Shredder: Learning Noise to Protect Privacy with Partial DNN Inference on the Edge (2019, methodology): “Homomorphic encryption, which can be used to implement SMC, is also used for privacy protection in neural networks.”
A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection (2019, methodology): “protection: cryptographic methods and differential privacy. Cryptographic methods such as homomorphic encryption[15, 77, 27, 33, 74, 145, 146, 204, 206, 112], and secure multi-party computation (SMC) [157, 36, 23, 51, 24, 101, 17, 62, 91, 181, 39, 65] are widely used in privacy-preserving machine le”
IRON: Functional Encryption using Intel SGX (2017, background): “A detailed comparison of these systems to Intel SGX is covered in [20].”
DarKnight: A Data Privacy Scheme for Training and Inference of Deep Neural Networks (2020, unknown): “Inference FHME[19], MiniONN [20], CryptoNets[21], Gazelle[22] SGXCMP[23], SecureML[24] Mlcapsule[7], ObliviousTEE[8], P-TEE[9], Slalom[10] Arden [25], NOffload[26], Shredder [27]”