YerbaBuena: Securing Deep Learning Inference Data via Enclave-based Ternary Model Partitioning

By Zhongshu Gu, Heqing Huang, Jialong Zhang, Dong Su, Hani Jamjoom, Ankita Lamba, Dimitrios E. Pendarakis, Ian M. Molloy. 2018

29 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Yoshua Bengio
2. Peter Henderson
3. Sara Hooker
4. Sergey Levine
5. Bo Li
6. David Krueger
7. Pang Wei Koh
8. Shagun Sodhani

Citations in the corpus

• Toward Trustworthy AI Development: Mechanisms for Supporting Verifiable Claims (2020, background): “67 Demonstrations to date have been carried out on commodity hardware (CPUs [113] [114] and GPUs [115]) or have secure and verifiable outsourcing of parts of the computation to less secure hardware [116] [117], rather than on hardware directly optimized for machine learning (such as TPUs).”
• PPFL: privacy-preserving federated learning with trusted execution environments (2021, methodology): “Running ML inside TEEs can hide model parameters from REE adversaries and consequently preserve privacy, as already used for light data analytics on servers [54, 62] and for heavy computations such as DNN training [18, 23, 49, 70].”
• DarkneTZ: towards model privacy at the edge using trusted execution environments (2020, background): “, [17] ar X iv :2 00 4.”