Towards the Science of Security and Privacy in Machine Learning (1611.03814v1)

Published 11 Nov 2016 in cs.CR and cs.LG

Abstract: Advances in ML in recent years have enabled a dizzying array of applications such as data analytics, autonomous systems, and security diagnostics. ML is now pervasive---new systems and models are being deployed in every domain imaginable, leading to rapid and widespread deployment of software based inference and decision making. There is growing recognition that ML exposes new vulnerabilities in software systems, yet the technical community's understanding of the nature and extent of these vulnerabilities remains limited. We systematize recent findings on ML security and privacy, focusing on attacks identified on these systems and defenses crafted to date. We articulate a comprehensive threat model for ML, and categorize attacks and defenses within an adversarial framework. Key insights resulting from works both in the ML and security communities are identified and the effectiveness of approaches are related to structural elements of ML algorithms and the data used to train them. We conclude by formally exploring the opposing relationship between model accuracy and resilience to adversarial manipulation. Through these explorations, we show that there are (possibly unavoidable) tensions between model complexity, accuracy, and resilience that must be calibrated for the environments in which they will be used.

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Authors (4)

Nicolas Papernot (123 papers)
Patrick McDaniel (70 papers)
Arunesh Sinha (35 papers)
Michael Wellman (5 papers)

Citations (463)

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Summary

Security and Privacy in Machine Learning: A Comprehensive Analysis

The paper "SoK: Towards the Science of Security and Privacy in Machine Learning" by Papernot et al. provides an extensive systematization of knowledge in the burgeoning field of adversarial machine learning. By focusing on the vulnerabilities, attacks, and defenses of ML systems, the authors present a structured overview that spans multiple research domains, including security and theory of computation.

Key Contributions

The paper articulates a robust threat model for ML systems organized around the pipeline architecture commonly used in these systems. This model provides a foundational basis to categorize attacks and defenses in an adversarial framework.

Attack Surface and Adversarial Models

The attack surface is divided into two main phases: training and inference. Attacks can be categorized based on the adversaries' knowledge: white-box and black-box. The authors detail various attack vectors illustrating how adversaries can manipulate input data, infer models, or modify training data to compromise a system’s integrity, confidentiality, or availability.

Theoretical Insights

A significant insight offered is the formal exploration of the "no free lunch" theorem in the context of adversarial learning. It elucidates the inherent trade-offs between model complexity, accuracy, and robustness, highlighting the tensions between ensuring prediction precision and resilience against adversarial manipulation under constrained data environments.

Implications and Future Directions

Security Implications: The findings underline the critical need for ML models that are inherently robust to distribution drifts and adversarial manipulations. Current defenses, such as adversarial training and regularization techniques, show promise but require further refinement to address sophisticated attack methods like those utilizing model transferability.

Privacy Implications: The paper also addresses privacy concerns, emphasizing that ML models should not inadvertently memorize or reveal training data. The authors propose using differentially private algorithms and homomorphic encryption to safeguard sensitive information.

Theoretical Implications: By connecting practical attack methodologies with learning-theoretic frameworks, the paper encourages the development of models that can withstand adversarial conditions while maintaining high accuracy. This requires a delicate balance in model complexity to avoid overfitting and to accommodate varying data distributions.

Conclusion

The paper by Papernot et al. provides a comprehensive roadmap for research at the intersection of machine learning, security, and privacy, urging the ML community to develop models that harmonize accuracy with security and privacy. By emphasizing the intricacies of the adversarial landscape, it lays a foundation for developing next-generation, robust ML systems capable of withstanding evolving threats in diverse application domains.

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