TENNOR: Trustworthy Execution for Neural Networks through Obliviousness and Retrievals

• URL: http://arxiv.org/abs/2605.07160v1
• Date: Fri, 08 May 2026 02:46:24 GMT
• Title: TENNOR: Trustworthy Execution for Neural Networks through Obliviousness and Retrievals
• Abstract summary: Training neural networks on sensitive data in untrusted cloud environments requires simultaneously achieving computational efficiency and rigorous privacy guarantees.<n>We present TENNOR, a system that resolves this tension by co-designing the neural network training pipeline with doubly oblivious primitives.<n> TENNOR recasts sparse neuron activation as a locality-sensitive hashing (LSH) retrieval problem, reducing secure sparsification to doubly oblivious accesses over an LSH data structure.
• Score: 11.751398157718013
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Training wide neural networks on sensitive data in untrusted cloud environments requires simultaneously achieving computational efficiency and rigorous privacy guarantees. Sparsification techniques, essential for scalable training of wide layers, expose input-dependent memory-access patterns (i.e., leakage) that are visible and can be exploited by a host OS/hypervisor, even when computation is protected by a Trusted Execution Environment. We present TENNOR, a system that resolves this tension by co-designing the neural network training pipeline with doubly oblivious primitives, eliminating access-pattern leakage while also utilizing adaptive sparsification. TENNOR recasts sparse neuron activation as a locality-sensitive hashing (LSH) retrieval problem, reducing secure sparsification to doubly oblivious accesses over an LSH data structure. To eliminate the prohibitive storage cost of ``multi-table'' LSH, we introduce Multi-Probe Winner-Take-All (MP-WTA): the first multi-probe scheme for rank-based LSH, achieving a 50x reduction in (hash table) memory while preserving model accuracy. We evaluate TENNOR on extreme multi-label classification benchmarks with output layers of up to 325K neurons inside an Intel TDX Trusted Domain, achieving speedups of 13x--470x over a Path ORAM baseline and reducing a 208-hour run to about 26 minutes.

Related papers

• TsetlinWiSARD: On-Chip Training of Weightless Neural Networks using Tsetlin Automata on FPGAs [0.4499833362998488]
  We propose TsetlinWiSARD, a training approach for Weightless Neural Network (WNN)<n>It overcomes the overfitting of WiSARD's one-shot training with iterative optimization, while maintaining simple, continuous binary feedback for efficient on-chip training.<n>Our approach provides over 1000x faster training when compared with the traditional WiSARD implementation of WNNs.
  arXiv  Detail & Related papers  (2026-03-25T11:08:18Z)
• HALO: Semantic-Aware Distributed LLM Inference in Lossy Edge Network [50.33808558714122]
  Large language models' (LLMs) inference at the edge can facilitate prompt service responsiveness while protecting user privacy.<n>We propose HALO, a novel framework that can boost the distributed LLM inference in lossy edge network.<n> Experimental results from a Raspberry Pi cluster demonstrate that HALO achieves a 3.41x end-to-end speedup for LLaMA-series LLMs under unreliable network conditions.
  arXiv  Detail & Related papers  (2026-01-16T07:37:23Z)
• Efficient Memristive Spiking Neural Networks Architecture with Supervised In-Situ STDP Method [0.0]
  Memristor-based Spiking Neural Networks (SNNs) with temporal spike encoding enable ultra-low-energy computation.<n>This paper presents a circuit-level memristive spiking neural network (SNN) architecture trained using a proposed novel supervised in-situ learning algorithm.
  arXiv  Detail & Related papers  (2025-07-28T17:09:48Z)
• Provably-Safe Neural Network Training Using Hybrid Zonotope Reachability Analysis [2.1198879079315573]
  It is difficult to enforce constraints on neural networks in safety-critical control applications.<n>We propose a method that can encourage exact image of a non- hybrid input set for a neural network with rectified linear unit (ReLU) nonlinearities.
  arXiv  Detail & Related papers  (2025-01-22T17:13:48Z)
• A 65 nm Bayesian Neural Network Accelerator with 360 fJ/Sample In-Word GRNG for AI Uncertainty Estimation [4.828223001507891]
  We present an ASIC that integrates 360 fJ/Sample Gaussian RNG directly into the memory words.<n>This integration reduces RNG overhead and enables fully-parallel compute-in-memory operations for BNNs.<n>The prototype chip achieves 5.12 GSa/s RNG throughput and 102 GOp/s neural network throughput while occupying 0.45 mm2.
  arXiv  Detail & Related papers  (2025-01-08T15:47:04Z)
• Optimal Gradient Checkpointing for Sparse and Recurrent Architectures using Off-Chip Memory [0.8321953606016751]
  We introduce memory-efficient gradient checkpointing strategies tailored for the general class of sparse RNNs and Spiking Neural Networks.<n>We find that Double Checkpointing emerges as the most effective method, optimizing the use of local memory resources while minimizing recomputation overhead.
  arXiv  Detail & Related papers  (2024-12-16T14:23:31Z)
• NeuralFuse: Learning to Recover the Accuracy of Access-Limited Neural Network Inference in Low-Voltage Regimes [50.00272243518593]
  Deep neural networks (DNNs) have become ubiquitous in machine learning, but their energy consumption remains problematically high.<n>We have developed NeuralFuse, a novel add-on module that handles the energy-accuracy tradeoff in low-voltage regimes.<n>At a 1% bit-error rate, NeuralFuse can reduce access energy by up to 24% while recovering accuracy by up to 57%.
  arXiv  Detail & Related papers  (2023-06-29T11:38:22Z)
• CorrectNet: Robustness Enhancement of Analog In-Memory Computing for Neural Networks by Error Suppression and Compensation [4.570841222958966]
  We propose a framework to enhance the robustness of neural networks under variations and noise. We show that inference accuracy of neural networks can be recovered from as low as 1.69% under variations and noise.
  arXiv  Detail & Related papers  (2022-11-27T19:13:33Z)
• Robust Training and Verification of Implicit Neural Networks: A Non-Euclidean Contractive Approach [64.23331120621118]
  This paper proposes a theoretical and computational framework for training and robustness verification of implicit neural networks. We introduce a related embedded network and show that the embedded network can be used to provide an $ell_infty$-norm box over-approximation of the reachable sets of the original network. We apply our algorithms to train implicit neural networks on the MNIST dataset and compare the robustness of our models with the models trained via existing approaches in the literature.
  arXiv  Detail & Related papers  (2022-08-08T03:13:24Z)
• Can pruning improve certified robustness of neural networks? [106.03070538582222]
  We show that neural network pruning can improve empirical robustness of deep neural networks (NNs) Our experiments show that by appropriately pruning an NN, its certified accuracy can be boosted up to 8.2% under standard training. We additionally observe the existence of certified lottery tickets that can match both standard and certified robust accuracies of the original dense models.
  arXiv  Detail & Related papers  (2022-06-15T05:48:51Z)
• Rapid Structural Pruning of Neural Networks with Set-based Task-Adaptive Meta-Pruning [83.59005356327103]
  A common limitation of most existing pruning techniques is that they require pre-training of the network at least once before pruning. We propose STAMP, which task-adaptively prunes a network pretrained on a large reference dataset by generating a pruning mask on it as a function of the target dataset. We validate STAMP against recent advanced pruning methods on benchmark datasets.
  arXiv  Detail & Related papers  (2020-06-22T10:57:43Z)
• Neural Networks and Value at Risk [59.85784504799224]
  We perform Monte-Carlo simulations of asset returns for Value at Risk threshold estimation. Using equity markets and long term bonds as test assets, we investigate neural networks. We find our networks when fed with substantially less data to perform significantly worse.
  arXiv  Detail & Related papers  (2020-05-04T17:41:59Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.