mlsanityData to 14 Oct 2026

Privacy and Integrity Preserving Training Using Trusted Hardware

arXiv 2105.00334 ↗First posted 1 May 2021cs.CR cs.AR cs.LGcited by 0

Not on the map. Why?

Abstract (arXiv)

Privacy and security-related concerns are growing as machine learning reaches diverse application domains. The data holders want to train with private data while exploiting accelerators, such as GPUs, that are hosted in the cloud. However, Cloud systems are vulnerable to attackers that compromise the privacy of data and integrity of computations. This work presents DarKnight, a framework for large DNN training while protecting input privacy and computation integrity. DarKnight relies on cooperative execution between trusted execution environments (TEE) and accelerators, where the TEE provides privacy and integrity verification, while accelerators perform the computation heavy linear algebraic operations.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 17 of its 38 references that are in the map. First posted on arXiv 1 May 2021.

Builds on

17 of 38 references.

References · 17

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,364
  3. 2014ImageNet Large Scale Visual Recognition Challengecited by 8,498
  4. 2018MobileNetV2: Inverted Residuals and Linear Bottleneckscited by 4,795
  5. 2018Gazelle: A Low Latency Framework for Secure Neural Network Inferencecited by 193
  6. 2018Slalom: Fast, Verifiable and Private Execution of Neural Networks in Trusted Hardwarecited by 102
  7. 2020InstaHide: Instance-hiding Schemes for Private Distributed Learningcited by 90
  8. 2020Privacy in Deep Learning: A Surveycited by 71
  9. 2018Lagrange Coded Computing: Optimal Design for Resiliency, Security and Privacycited by 66
  10. 2020DarkneTZ: Towards Model Privacy at the Edge using Trusted Execution Environmentscited by 58
  11. 2018Chiron: Privacy-preserving Machine Learning as a Servicecited by 40
  12. 2020Is Private Learning Possible with Instance Encoding?cited by 22
5 more
  1. 2019CodedPrivateML: A Fast and Privacy-Preserving Framework for Distributed Machine Learningcited by 20
  2. 2018Efficient Deep Learning on Multi-Source Private Datacited by 17
  3. 2019Privacy-Preserving Inference in Machine Learning Services Using Trusted Execution Environmentscited by 17
  4. 2020Secure multiparty computations in floating-point arithmeticcited by 8
  5. 2020GOAT: GPU Outsourcing of Deep Learning Training With Asynchronous Probabilistic Integrity Verification Inside Trusted Execution Environmentcited by 6

Built on by

Not cited yet.