Model Inversion Attacks: A Survey of Approaches and Countermeasures

• URL: http://arxiv.org/abs/2411.10023v1
• Date: Fri, 15 Nov 2024 08:09:28 GMT
• Title: Model Inversion Attacks: A Survey of Approaches and Countermeasures
• Authors: Zhanke Zhou, Jianing Zhu, Fengfei Yu, Xuan Li, Xiong Peng, Tongliang Liu, Bo Han,
• Abstract summary: Recently, a new type of privacy attack, the model inversion attacks (MIAs), aims to extract sensitive features of private data for training. Despite the significance, there is a lack of systematic studies that provide a comprehensive overview and deeper insights into MIAs. This survey aims to summarize up-to-date MIA methods in both attacks and defenses.
• Score: 59.986922963781
• License:
• Abstract: The success of deep neural networks has driven numerous research studies and applications from Euclidean to non-Euclidean data. However, there are increasing concerns about privacy leakage, as these networks rely on processing private data. Recently, a new type of privacy attack, the model inversion attacks (MIAs), aims to extract sensitive features of private data for training by abusing access to a well-trained model. The effectiveness of MIAs has been demonstrated in various domains, including images, texts, and graphs. These attacks highlight the vulnerability of neural networks and raise awareness about the risk of privacy leakage within the research community. Despite the significance, there is a lack of systematic studies that provide a comprehensive overview and deeper insights into MIAs across different domains. This survey aims to summarize up-to-date MIA methods in both attacks and defenses, highlighting their contributions and limitations, underlying modeling principles, optimization challenges, and future directions. We hope this survey bridges the gap in the literature and facilitates future research in this critical area. Besides, we are maintaining a repository to keep track of relevant research at https://github.com/AndrewZhou924/Awesome-model-inversion-attack.

Related papers

• New Emerged Security and Privacy of Pre-trained Model: a Survey and Outlook [54.24701201956833]
  Security and privacy issues have undermined users' confidence in pre-trained models. Current literature lacks a clear taxonomy of emerging attacks and defenses for pre-trained models. This taxonomy categorizes attacks and defenses into No-Change, Input-Change, and Model-Change approaches.
  arXiv  Detail & Related papers  (2024-11-12T10:15:33Z)
• 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)
• A Survey of Privacy-Preserving Model Explanations: Privacy Risks, Attacks, and Countermeasures [50.987594546912725]
  Despite a growing corpus of research in AI privacy and explainability, there is little attention on privacy-preserving model explanations. This article presents the first thorough survey about privacy attacks on model explanations and their countermeasures.
  arXiv  Detail & Related papers  (2024-03-31T12:44:48Z)
• Topological safeguard for evasion attack interpreting the neural networks' behavior [0.0]
  In this work, a novel detector of evasion attacks is developed. It focuses on the information of the activations of the neurons given by the model when an input sample is injected. For this purpose, a huge data preprocessing is required to introduce all this information in the detector.
  arXiv  Detail & Related papers  (2024-02-12T08:39:40Z)
• Privacy Leakage on DNNs: A Survey of Model Inversion Attacks and Defenses [40.77270226912783]
  Model Inversion (MI) attacks disclose private information about the training dataset by abusing access to the trained models. Despite the rapid advances in the field, we lack a comprehensive and systematic overview of existing MI attacks and defenses. We elaborately analyze and compare numerous recent attacks and defenses on Deep Neural Networks (DNNs) across multiple modalities and learning tasks.
  arXiv  Detail & Related papers  (2024-02-06T14:06:23Z)
• A Unified View of Differentially Private Deep Generative Modeling [60.72161965018005]
  Data with privacy concerns comes with stringent regulations that frequently prohibited data access and data sharing. Overcoming these obstacles is key for technological progress in many real-world application scenarios that involve privacy sensitive data. Differentially private (DP) data publishing provides a compelling solution, where only a sanitized form of the data is publicly released.
  arXiv  Detail & Related papers  (2023-09-27T14:38:16Z)
• Poisoning Attacks and Defenses on Artificial Intelligence: A Survey [3.706481388415728]
  Data poisoning attacks represent a type of attack that consists of tampering the data samples fed to the model during the training phase, leading to a degradation in the models accuracy during the inference phase. This work compiles the most relevant insights and findings found in the latest existing literatures addressing this type of attacks. A thorough assessment is performed on the reviewed works, comparing the effects of data poisoning on a wide range of ML models in real-world conditions.
  arXiv  Detail & Related papers  (2022-02-21T14:43:38Z)
• Adversarial Machine Learning In Network Intrusion Detection Domain: A Systematic Review [0.0]
  It has been found that deep learning models are vulnerable to data instances that can mislead the model to make incorrect classification decisions. This survey explores the researches that employ different aspects of adversarial machine learning in the area of network intrusion detection.
  arXiv  Detail & Related papers  (2021-12-06T19:10:23Z)
• Privacy in Deep Learning: A Survey [16.278779275923448]
  The ever-growing advances of deep learning in many areas have led to the adoption of Deep Neural Networks (DNNs) in production systems. The availability of large datasets and high computational power are the main contributors to these advances. This poses serious privacy concerns as this data can be misused or leaked through various vulnerabilities.
  arXiv  Detail & Related papers  (2020-04-25T23:47:25Z)
• Survey of Network Intrusion Detection Methods from the Perspective of the Knowledge Discovery in Databases Process [63.75363908696257]
  We review the methods that have been applied to network data with the purpose of developing an intrusion detector. We discuss the techniques used for the capture, preparation and transformation of the data, as well as, the data mining and evaluation methods. As a result of this literature review, we investigate some open issues which will need to be considered for further research in the area of network security.
  arXiv  Detail & Related papers  (2020-01-27T11:21:05Z)

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.