A Novel Access Control and Privacy-Enhancing Approach for Models in Edge Computing

• URL: http://arxiv.org/abs/2411.03847v1
• Date: Wed, 06 Nov 2024 11:37:30 GMT
• Title: A Novel Access Control and Privacy-Enhancing Approach for Models in Edge Computing
• Authors: Peihao Li,
• Abstract summary: We propose a novel model access control method tailored for edge computing environments. This method leverages image style as a licensing mechanism, embedding style recognition into the model's operational framework. By restricting the input data to the edge model, this approach not only prevents attackers from gaining unauthorized access to the model but also enhances the privacy of data on terminal devices.
• Score: 0.26107298043931193
• License:
• Abstract: With the widespread adoption of edge computing technologies and the increasing prevalence of deep learning models in these environments, the security risks and privacy threats to models and data have grown more acute. Attackers can exploit various techniques to illegally obtain models or misuse data, leading to serious issues such as intellectual property infringement and privacy breaches. Existing model access control technologies primarily rely on traditional encryption and authentication methods; however, these approaches exhibit significant limitations in terms of flexibility and adaptability in dynamic environments. Although there have been advancements in model watermarking techniques for marking model ownership, they remain limited in their ability to proactively protect intellectual property and prevent unauthorized access. To address these challenges, we propose a novel model access control method tailored for edge computing environments. This method leverages image style as a licensing mechanism, embedding style recognition into the model's operational framework to enable intrinsic access control. Consequently, models deployed on edge platforms are designed to correctly infer only on license data with specific style, rendering them ineffective on any other data. By restricting the input data to the edge model, this approach not only prevents attackers from gaining unauthorized access to the model but also enhances the privacy of data on terminal devices. We conducted extensive experiments on benchmark datasets, including MNIST, CIFAR-10, and FACESCRUB, and the results demonstrate that our method effectively prevents unauthorized access to the model while maintaining accuracy. Additionally, the model shows strong resistance against attacks such as forged licenses and fine-tuning. These results underscore the method's usability, security, and robustness.

Related papers

• TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Models [12.253529209143197]
  TSDP is a method that protects privacy-sensitive weights within TEEs and offloads insensitive weights to GPUs. We introduce a novel partition before training strategy, which effectively separates privacy-sensitive weights from other components of the model. Our evaluation demonstrates that our approach can offer full model protection with a computational cost reduced by a factor of 10.
  arXiv  Detail & Related papers  (2024-11-15T04:52:11Z)
• EnTruth: Enhancing the Traceability of Unauthorized Dataset Usage in Text-to-image Diffusion Models with Minimal and Robust Alterations [73.94175015918059]
  We introduce a novel approach, EnTruth, which Enhances Traceability of unauthorized dataset usage. By strategically incorporating the template memorization, EnTruth can trigger the specific behavior in unauthorized models as the evidence of infringement. Our method is the first to investigate the positive application of memorization and use it for copyright protection, which turns a curse into a blessing.
  arXiv  Detail & Related papers  (2024-06-20T02:02:44Z)
• Unlearning Backdoor Attacks through Gradient-Based Model Pruning [10.801476967873173]
  We propose a novel approach to counter backdoor attacks by treating their mitigation as an unlearning task. Our approach offers simplicity and effectiveness, rendering it well-suited for scenarios with limited data availability.
  arXiv  Detail & Related papers  (2024-05-07T00:36:56Z)
• ModelShield: Adaptive and Robust Watermark against Model Extraction Attack [58.46326901858431]
  Large language models (LLMs) demonstrate general intelligence across a variety of machine learning tasks. adversaries can still utilize model extraction attacks to steal the model intelligence encoded in model generation. Watermarking technology offers a promising solution for defending against such attacks by embedding unique identifiers into the model-generated content.
  arXiv  Detail & Related papers  (2024-05-03T06:41:48Z)
• Ungeneralizable Examples [70.76487163068109]
  Current approaches to creating unlearnable data involve incorporating small, specially designed noises. We extend the concept of unlearnable data to conditional data learnability and introduce textbfUntextbfGeneralizable textbfExamples (UGEs) UGEs exhibit learnability for authorized users while maintaining unlearnability for potential hackers.
  arXiv  Detail & Related papers  (2024-04-22T09:29:14Z)
• 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)
• Rethinking Privacy in Machine Learning Pipelines from an Information Flow Control Perspective [16.487545258246932]
  Modern machine learning systems use models trained on ever-growing corpora. metadata such as ownership, access control, or licensing information is ignored during training. We take an information flow control perspective to describe machine learning systems.
  arXiv  Detail & Related papers  (2023-11-27T13:14:39Z)
• Safe and Robust Watermark Injection with a Single OoD Image [90.71804273115585]
  Training a high-performance deep neural network requires large amounts of data and computational resources. We propose a safe and robust backdoor-based watermark injection technique. We induce random perturbation of model parameters during watermark injection to defend against common watermark removal attacks.
  arXiv  Detail & Related papers  (2023-09-04T19:58:35Z)
• Self-Destructing Models: Increasing the Costs of Harmful Dual Uses of Foundation Models [103.71308117592963]
  We present an algorithm for training self-destructing models leveraging techniques from meta-learning and adversarial learning. In a small-scale experiment, we show MLAC can largely prevent a BERT-style model from being re-purposed to perform gender identification.
  arXiv  Detail & Related papers  (2022-11-27T21:43:45Z)
• Non-Transferable Learning: A New Approach for Model Verification and Authorization [7.686781778077341]
  There are two common protection methods: ownership verification and usage authorization. We propose Non-Transferable Learning (NTL), a novel approach that captures the exclusive data representation in the learned model. Our NTL-based authorization approach provides data-centric usage protection by significantly degrading the performance of usage on unauthorized data.
  arXiv  Detail & Related papers  (2021-06-13T04:57:16Z)
• Anonymizing Machine Learning Models [0.0]
  Anonymized data is exempt from obligations set out in regulations such as the EU General Data Protection Regulation. We propose a method that is able to achieve better model accuracy by using the knowledge encoded within the trained model. We also demonstrate that our approach has a similar, and sometimes even better ability to prevent membership attacks as approaches based on differential privacy.
  arXiv  Detail & Related papers  (2020-07-26T09:29:03Z)

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.