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AIJack: Let's Hijack AI! Security and Privacy Risk Simulator for Machine Learning (2312.17667v2)

Published 29 Dec 2023 in cs.LG and cs.CR

Abstract: This paper introduces AIJack, an open-source library designed to assess security and privacy risks associated with the training and deployment of machine learning models. Amid the growing interest in big data and AI, advancements in machine learning research and business are accelerating. However, recent studies reveal potential threats, such as the theft of training data and the manipulation of models by malicious attackers. Therefore, a comprehensive understanding of machine learning's security and privacy vulnerabilities is crucial for the safe integration of machine learning into real-world products. AIJack aims to address this need by providing a library with various attack and defense methods through a unified API. The library is publicly available on GitHub (https://github.com/Koukyosyumei/AIJack).

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References (43)
  1. Evasion attacks against machine learning at test time. In Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2013, Prague, Czech Republic, September 23-27, 2013, Proceedings, Part III 13, pages 387–402. Springer, 2013.
  2. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572, 2014.
  3. Poisoning attacks against support vector machines. arXiv preprint arXiv:1206.6389, 2012.
  4. Certified robustness to adversarial examples with differential privacy. In 2019 IEEE symposium on security and privacy (SP), pages 656–672. IEEE, 2019.
  5. {{\{{Cost-Aware}}\}} robust tree ensembles for security applications. In 30th USENIX Security Symposium (USENIX Security 21), pages 2291–2308, 2021.
  6. Model assertions for debugging machine learning. 2018.
  7. Complaint-driven training data debugging for query 2.0. In Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data, pages 1317–1334, 2020.
  8. Deepxplore: Automated whitebox testing of deep learning systems. In Proceedings of the 26th Symposium on Operating Systems Principles, SOSP ’17, page 1–18, New York, NY, USA, 2017. Association for Computing Machinery.
  9. Model inversion attacks that exploit confidence information and basic countermeasures. In Proceedings of the 22nd ACM SIGSAC Conference on Computer and Communications Security, CCS ’15, page 1322–1333, New York, NY, USA, 2015. Association for Computing Machinery.
  10. Deep leakage from gradients. Advances in neural information processing systems, 32, 2019.
  11. Membership inference attacks against machine learning models. In 2017 IEEE symposium on security and privacy (SP), pages 3–18. IEEE, 2017.
  12. Deep learning with differential privacy. In Proceedings of the 2016 ACM SIGSAC conference on computer and communications security, pages 308–318, 2016.
  13. Mondrian multidimensional k-anonymity. In 22nd International Conference on Data Engineering (ICDE’06), pages 25–25, 2006.
  14. Pascal Paillier. Public-key cryptosystems based on composite degree residuosity classes. In International conference on the theory and applications of cryptographic techniques, pages 223–238. Springer, 1999.
  15. Communication-efficient learning of deep networks from decentralized data. In Artificial intelligence and statistics, pages 1273–1282. PMLR, 2017.
  16. Split learning for health: Distributed deep learning without sharing raw patient data. arXiv preprint arXiv:1812.00564, 2018.
  17. Pytorch: An imperative style, high-performance deep learning library. Advances in neural information processing systems, 32, 2019.
  18. Scikit-learn: Machine learning in python. the Journal of machine Learning research, 12:2825–2830, 2011.
  19. Federated optimization in heterogeneous networks. Proceedings of Machine learning and systems, 2:429–450, 2020.
  20. Fedmd: Heterogenous federated learning via model distillation. arXiv preprint arXiv:1910.03581, 2019.
  21. Fedgems: Federated learning of larger server models via selective knowledge fusion. arXiv preprint arXiv:2110.11027, 2021.
  22. Distillation-based semi-supervised federated learning for communication-efficient collaborative training with non-iid private data. IEEE Transactions on Mobile Computing, 22(1):191–205, 2021.
  23. Model-contrastive federated learning. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 10713–10722, 2021.
  24. Fedexp: Speeding up federated averaging via extrapolation. arXiv preprint arXiv:2301.09604, 2023.
  25. Secureboost: A lossless federated learning framework. IEEE Intelligent Systems, 36(6):87–98, 2021.
  26. idlg: Improved deep leakage from gradients. arXiv preprint arXiv:2001.02610, 2020.
  27. Inverting gradients-how easy is it to break privacy in federated learning? Advances in Neural Information Processing Systems, 33:16937–16947, 2020.
  28. A framework for evaluating gradient leakage attacks in federated learning. arXiv preprint arXiv:2004.10397, 2020.
  29. See through gradients: Image batch recovery via gradinversion. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 16337–16346, 2021.
  30. Deep models under the gan: information leakage from collaborative deep learning. In Proceedings of the 2017 ACM SIGSAC conference on computer and communications security, pages 603–618, 2017.
  31. Label leakage and protection in two-party split learning. arXiv preprint arXiv:2102.08504, 2021.
  32. Mpaf: Model poisoning attacks to federated learning based on fake clients. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 3396–3404, 2022.
  33. Dba: Distributed backdoor attacks against federated learning. In International Conference on Learning Representations, 2020.
  34. How to backdoor federated learning. In Silvia Chiappa and Roberto Calandra, editors, Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics, volume 108 of Proceedings of Machine Learning Research, pages 2938–2948. PMLR, 26–28 Aug 2020.
  35. Free-riders in federated learning: Attacks and defenses. arXiv preprint arXiv:1911.12560, 2019.
  36. A tale of two models: Constructing evasive attacks on edge models. Proceedings of Machine Learning and Systems, 4:414–429, 2022.
  37. Private adaptive optimization with side information. In International Conference on Machine Learning, pages 13086–13105. PMLR, 2022.
  38. Dplis: Boosting utility of differentially private deep learning via randomized smoothing. arXiv preprint arXiv:2103.01496, 2021.
  39. Provable defense against privacy leakage in federated learning from representation perspective. arXiv preprint arXiv:2012.06043, 2020.
  40. Mitigating sybils in federated learning poisoning. arXiv preprint arXiv:1808.04866, 2018.
  41. Improving robustness to model inversion attacks via mutual information regularization. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 35, pages 11666–11673, 2021.
  42. Sparse communication for distributed gradient descent. In Martha Palmer, Rebecca Hwa, and Sebastian Riedel, editors, Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 440–445, Copenhagen, Denmark, September 2017. Association for Computational Linguistics.
  43. Opacus: User-friendly differential privacy library in pytorch. arXiv preprint arXiv:2109.12298, 2021.
Citations (2)
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Summary

  • The paper introduces AIJack, an open-source toolkit to simulate and evaluate security and privacy vulnerabilities in ML models.
  • It employs diverse attack simulations including evasion, poisoning, and model inversion to test model robustness.
  • The toolkit integrates defense mechanisms like differential privacy and federated learning to enhance secure ML deployments.

Summary of "AIJack: Vulnerability Analysis Toolkit for Machine Learning Models"

The paper introduces AIJack, an open-source library designed to evaluate the security and privacy vulnerabilities inherent in ML models. As ML-driven applications become ubiquitous, they are increasingly targeted by adversarial attacks that exploit these vulnerabilities. The paper aims to equip practitioners with the tools necessary to assess and bolster the robustness of ML models through a unified API that supports various attack and defense techniques.

Background and Motivation

Recent research highlights the vulnerability of machine learning models to both adversarial attacks and privacy violations. Adversaries can compromise model integrity through methods such as Evasion Attacks, which involve crafting data inputs that cause models to misclassify, and Poisoning Attacks, where corrupted data is injected during training to degrade model performance. Deep learning models are notably susceptible due to their sensitivity to minute feature perturbations, emphasizing the need for effective vulnerability assessment tools.

In terms of privacy concerns, adversarial techniques such as Model Inversion Attacks can reconstruct training data from model parameters. Membership Inference Attacks further exacerbate privacy issues by determining whether specific data was part of the training set. Addressing these challenges requires a comprehensive approach to safeguard ML systems.

Features of AIJack

AIJack's library is curated to facilitate both the simulation of attacks and the implementation of defensive strategies. Highlights include:

  • Attack Simulations: The library supports diverse attack models such as Gradient Inversion, Backdoor, and Membership Inference attacks, offering a platform to test model resilience.
  • Defense Mechanisms: AIJack includes defense strategies like differential privacy, utilizing differential private SGD to protect training data, and homomorphic encryption to perform secure computations.
  • Collaborative Learning: There is a strong emphasis on federated and split learning paradigms within the library, accommodating secure, decentralized model training without data centralization.

Implementation and Use Cases

AIJack is particularly focused on federated learning (FL) environments. The library provides implementations for Horizontal FL algorithms such as FedAVG and FedProx, and Vertical FL methods like SplitNN, along with various attack and defense scenarios applicable to these settings. For instance, integrating AttackManager with the FedAVG framework enables practitioners to test the robustness of federated learning models against gradient inversion attacks, thus providing real-world applicability in enhancing model security.

Implications and Future Developments

AIJack represents a significant step forward in addressing security and privacy concerns within the ML ecosystem. By offering a scalable, open-source tool, the paper not only addresses immediate model vulnerabilities but also sets the stage for further research into more intricate defense mechanisms. Future developments could extend AIJack’s capabilities to support advanced cryptographic techniques and real-time threat detection in more dynamic learning environments.

Overall, AIJack is poised to enhance the security landscape of ML models, offering researchers and practitioners an essential resource in the continuous effort to fortify machine learning systems against evolving threats. As the field of AI continues to advance, the toolkit's development will likely evolve to integrate novel algorithms and defensive strategies, contributing to the broader objective of secure AI deployment.

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  1. GitHub - Koukyosyumei/AIJack: Security and Privacy Risk Simulator for Machine Learning (arXiv:2312.17667) (344 stars)
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