PrivDNN: A Secure Multi-Party Computation Framework for Deep Learning using Partial DNN Encryption

• URL: http://arxiv.org/abs/2607.21895v1
• Date: Fri, 24 Jul 2026 01:52:18 GMT
• Title: PrivDNN: A Secure Multi-Party Computation Framework for Deep Learning using Partial DNN Encryption
• Abstract summary: In a secure multi-party scenario, both the model and the data are considered proprietary, i.e., the model owner does not want to reveal the highly valuable DL model to the user.<n>In this paper, we present a novel solution, namely, PrivDNN, which offloads the computation to the user side by sharing an encrypted deep learning model with them.
• Score: 23.75854815791631
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In the past decade, we have witnessed an exponential growth of deep learning models, platforms, and applications. While existing DL applications and Machine Learning as a service (MLaaS) frameworks assume fully trusted models, the need for privacy-preserving DNN evaluation arises. In a secure multi-party computation scenario, both the model and the data are considered proprietary, i.e., the model owner does not want to reveal the highly valuable DL model to the user, while the user does not wish to disclose their private data samples either. Conventional privacy-preserving deep learning solutions ask the users to send encrypted samples to the model owners, who must handle the heavy lifting of ciphertext-domain computation with homomorphic encryption. In this paper, we present a novel solution, namely, PrivDNN, which (1) offloads the computation to the user side by sharing an encrypted deep learning model with them, (2) significantly improves the efficiency of DNN evaluation using partial DNN encryption, (3) ensures model accuracy and model privacy using a core neuron selection and encryption scheme. Experimental results show that PrivDNN reduces privacy-preserving DNN inference time and memory requirement by up to 97% while maintaining model performance and privacy. Codes can be found at https://github.com/LiangqinRen/PrivDNN

Related papers

• PrivSpike: Employing Homomorphic Encryption for Private Inference of Deep Spiking Neural Networks [0.08749675983608168]
  Spiking Neural Networks (SNNs) have emerged as an energy-efficient alternative to conventional deep learning approaches.<n> PRIVSPIKE is a privacy-preserving inference framework for SNNs using the CKKS homomorphic encryption scheme.<n>On a consumer-grade CPU, SNN LeNet-5 models achieved inference times of 28 seconds on MNIST and 212 seconds on Neuromorphic MNIST.
  arXiv  Detail & Related papers  (2025-10-05T02:11:40Z)
• Privacy Backdoors: Enhancing Membership Inference through Poisoning Pre-trained Models [112.48136829374741]
  In this paper, we unveil a new vulnerability: the privacy backdoor attack. When a victim fine-tunes a backdoored model, their training data will be leaked at a significantly higher rate than if they had fine-tuned a typical model. Our findings highlight a critical privacy concern within the machine learning community and call for a reevaluation of safety protocols in the use of open-source pre-trained models.
  arXiv  Detail & Related papers  (2024-04-01T16:50:54Z)
• Training Natural Language Processing Models on Encrypted Text for Enhanced Privacy [0.0]
  We propose a method for training NLP models on encrypted text data to mitigate data privacy concerns. Our results indicate that both encrypted and non-encrypted models achieve comparable performance.
  arXiv  Detail & Related papers  (2023-05-03T00:37:06Z)
• HE-MAN -- Homomorphically Encrypted MAchine learning with oNnx models [0.23624125155742057]
  homomorphic encryption (FHE) is a promising technique to enable individuals using ML services without giving up privacy. We introduce HE-MAN, an open-source machine learning toolset for privacy preserving inference with ONNX models and homomorphically encrypted data. Compared to prior work, HE-MAN supports a broad range of ML models in ONNX format out of the box without sacrificing accuracy.
  arXiv  Detail & Related papers  (2023-02-16T12:37:14Z)
• Privacy-Preserved Neural Graph Similarity Learning [99.78599103903777]
  We propose a novel Privacy-Preserving neural Graph Matching network model, named PPGM, for graph similarity learning. To prevent reconstruction attacks, the proposed model does not communicate node-level representations between devices. To alleviate the attacks to graph properties, the obfuscated features that contain information from both vectors are communicated.
  arXiv  Detail & Related papers  (2022-10-21T04:38:25Z)
• CrowdGuard: Federated Backdoor Detection in Federated Learning [39.58317527488534]
  This paper presents a novel defense mechanism, CrowdGuard, that effectively mitigates backdoor attacks in Federated Learning. CrowdGuard employs a server-located stacked clustering scheme to enhance its resilience to rogue client feedback. The evaluation results demonstrate that CrowdGuard achieves a 100% True-Positive-Rate and True-Negative-Rate across various scenarios.
  arXiv  Detail & Related papers  (2022-10-14T11:27:49Z)
• Just Fine-tune Twice: Selective Differential Privacy for Large Language Models [69.66654761324702]
  We propose a simple yet effective just-fine-tune-twice privacy mechanism to achieve SDP for large Transformer-based language models. Experiments show that our models achieve strong performance while staying robust to the canary insertion attack.
  arXiv  Detail & Related papers  (2022-04-15T22:36:55Z)
• NeuraCrypt: Hiding Private Health Data via Random Neural Networks for Public Training [64.54200987493573]
  We propose NeuraCrypt, a private encoding scheme based on random deep neural networks. NeuraCrypt encodes raw patient data using a randomly constructed neural network known only to the data-owner. We show that NeuraCrypt achieves competitive accuracy to non-private baselines on a variety of x-ray tasks.
  arXiv  Detail & Related papers  (2021-06-04T13:42:21Z)
• NN-EMD: Efficiently Training Neural Networks using Encrypted Multi-Sourced Datasets [7.067870969078555]
  Training a machine learning model over an encrypted dataset is an existing promising approach to address the privacy-preserving machine learning task. We propose a novel framework, NN-EMD, to train a deep neural network (DNN) model over multiple datasets collected from multiple sources. We evaluate our framework for performance with regards to the training time and model accuracy on the MNIST datasets.
  arXiv  Detail & Related papers  (2020-12-18T23:01:20Z)
• Towards Scalable and Privacy-Preserving Deep Neural Network via Algorithmic-Cryptographic Co-design [28.789702559193675]
  We propose SPNN - a Scalable and Privacy-preserving deep Neural Network learning framework. From cryptographic perspective, we propose using two types of cryptographic techniques, i.e., secret sharing and homomorphic encryption. Experimental results conducted on real-world datasets demonstrate the superiority of SPNN.
  arXiv  Detail & Related papers  (2020-12-17T02:26:16Z)
• Deep Serial Number: Computational Watermarking for DNN Intellectual Property Protection [53.40245698216239]
  DSN (Deep Serial Number) is a watermarking algorithm designed specifically for deep neural networks (DNNs) Inspired by serial numbers in safeguarding conventional software IP, we propose the first implementation of serial number embedding within DNNs.
  arXiv  Detail & Related papers  (2020-11-17T21:42:40Z)
• CryptoSPN: Privacy-preserving Sum-Product Network Inference [84.88362774693914]
  We present a framework for privacy-preserving inference of sum-product networks (SPNs) CryptoSPN achieves highly efficient and accurate inference in the order of seconds for medium-sized SPNs.
  arXiv  Detail & Related papers  (2020-02-03T14:49:18Z)

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.