mlsanityData to 7 Oct 2026

Slalom: Fast, Verifiable and Private Execution of Neural Networks in Trusted Hardware

arXiv 1806.03287 ↗First posted 8 Jun 2018stat.ML cs.CR cs.LGcited by 101

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Abstract (arXiv)

As Machine Learning (ML) gets applied to security-critical or sensitive domains, there is a growing need for integrity and privacy for outsourced ML computations. A pragmatic solution comes from Trusted Execution Environments (TEEs), which use hardware and software protections to isolate sensitive computations from the untrusted software stack. However, these isolation guarantees come at a price in performance, compared to untrusted alternatives. This paper initiates the study of high performance execution of Deep Neural Networks (DNNs) in TEEs by efficiently partitioning DNN computations between trusted and untrusted devices. Building upon an efficient outsourcing scheme for matrix multiplication, we propose Slalom, a framework that securely delegates execution of all linear layers in a DNN from a TEE (e.g., Intel SGX or Sanctum) to a faster, yet untrusted, co-located processor. We evaluate Slalom by running DNNs in an Intel SGX enclave, which selectively delegates work to an untrusted GPU. For canonical DNNs (VGG16, MobileNet and ResNet variants) we obtain 6x to 20x increases in throughput for verifiable inference, and 4x to 11x for verifiable and private inference.

Key facts. Cited by 101 papers in the map (data through 7 Oct 2026). First posted on arXiv 8 Jun 2018.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 25; 2022: 18; 2023: 12; 2024: 14; 2025: 11; 2026: 21.

101 papers.

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  1. 2024Personal LLM Agents: Insights and Survey about the Capability, Efficiency and Securitycited by 257
  2. 2021PPFL: Privacy-preserving Federated Learning with Trusted Execution Environmentscited by 93
  3. 2022Trustworthy Graph Neural Networks: Aspects, Methods and Trendscited by 88
  4. 2021Label Leakage and Protection in Two-party Split Learningcited by 87
  5. 2022Position: Considerations for Differentially Private Learning with Large-Scale Public Pretrainingcited by 86
  6. 2021A Study of Face Obfuscation in ImageNetcited by 83
  7. 2022Federated Learning Enables Big Data for Rare Cancer Boundary Detectioncited by 83
  8. 2021Differential Privacy for Text Analytics via Natural Text Sanitizationcited by 74
  9. 2021Handcrafted Backdoors in Deep Neural Networkscited by 57
  10. 2021CryptGPU: Fast Privacy-Preserving Machine Learning on the GPUcited by 46
  11. 2021DeepPayload: Black-box Backdoor Attack on Deep Learning Models through Neural Payload Injectioncited by 42
  12. 2021ModelDiff: Testing-Based DNN Similarity Comparison for Model Reuse Detectioncited by 33
38 more
  1. 2023Privacy and Fairness in Federated Learning: on the Perspective of Trade-offcited by 26
  2. 2022Verifiable and Provably Secure Machine Unlearningcited by 24
  3. 2023No Privacy Left Outside: On the (In-)Security of TEE-Shielded DNN Partition for On-Device MLcited by 21
  4. 2021Defending against Reconstruction Attack in Vertical Federated Learningcited by 20
  5. 2021Circa: Stochastic ReLUs for Private Deep Learningcited by 16
  6. 2022Aggregation Service for Federated Learning: An Efficient, Secure, and More Resilient Realizationcited by 16
  7. 2023GrOVe: Ownership Verification of Graph Neural Networks using Embeddingscited by 16
  8. 2024Rescriber: Smaller-LLM-Powered User-Led Data Minimization for LLM-Based Chatbotscited by 16
  9. 2021Citadel: Protecting Data Privacy and Model Confidentiality for Collaborative Learning with SGXcited by 14
  10. 2021Sisyphus: A Cautionary Tale of Using Low-Degree Polynomial Activations in Privacy-Preserving Deep Learningcited by 14
  11. 2022DarKnight: An Accelerated Framework for Privacy and Integrity Preserving Deep Learning Using Trusted Hardwarecited by 13
  12. 2022Machine Learning with Confidential Computing: A Systematization of Knowledgecited by 13
  13. 2021secureTF: A Secure TensorFlow Frameworkcited by 12
  14. 2023BAFFLE: A Baseline of Backpropagation-Free Federated Learningcited by 12
  15. 2022Decentralized Stochastic Optimization with Inherent Privacy Protectioncited by 11
  16. 2024TransLinkGuard: Safeguarding Transformer Models Against Model Stealing in Edge Deploymentcited by 11
  17. 2022ezDPS: An Efficient and Zero-Knowledge Machine Learning Inference Pipelinecited by 10
  18. 2023Blockchain-based Federated Learning with Secure Aggregation in Trusted Execution Environment for Internet-of-Thingscited by 10
  19. 2021SoK: Machine Learning Governancecited by 9
  20. 2022Can Foundation Models Help Us Achieve Perfect Secrecy?cited by 9
  21. 2021Separation of Powers in Federated Learningcited by 8
  22. 2023A Survey of Secure Computation Using Trusted Execution Environmentscited by 8
  23. 2024A Fast, Performant, Secure Distributed Training Framework For Large Language Modelcited by 8
  24. 2021Responsible and Regulatory Conform Machine Learning for Medicine: A Survey of Challenges and Solutionscited by 7
  25. 2022TEE-based decentralized recommender systems: The raw data sharing redemptioncited by 6
  26. 2023Samplable Anonymous Aggregation for Private Federated Data Analysiscited by 6
  27. 2021Forensicability of Deep Neural Network Inference Pipelinescited by 5
  28. 2021ApproxIFER: A Model-Agnostic Approach to Resilient and Robust Prediction Serving Systemscited by 5
  29. 2022Shielding Federated Learning Systems against Inference Attacks with ARM TrustZonecited by 5
  30. 2024PristiQ: A Co-Design Framework for Preserving Data Security of Quantum Learning in the Cloudcited by 5
  31. 2024CoreGuard: Safeguarding Foundational Capabilities of LLMs Against Model Stealing in Edge Deploymentcited by 5
  32. 2025Confidential LLM Inference: Performance and Cost Across CPU and GPU TEEscited by 5
  33. 2021Adaptive Verifiable Coded Computing: Towards Fast, Secure and Private Distributed Machine Learningcited by 4
  34. 2021Confidential Machine Learning Computation in Untrusted Environments: A Systems Security Perspectivecited by 4
  35. 2022MPC-Pipe: an Efficient Pipeline Scheme for Secure Multi-party Machine Learning Inferencecited by 4
  36. 2022GAN-based Domain Inference Attackcited by 4
  37. 2023All Rivers Run to the Sea: Private Learning with Asymmetric Flowscited by 4
  38. 2024Fortify Your Foundations: Practical Privacy and Security for Foundation Model Deployments In The Cloudcited by 4

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