Auditing Differentially Private Machine Learning: How Private is Private SGD? (2006.07709v1)

Abstract: We investigate whether Differentially Private SGD offers better privacy in practice than what is guaranteed by its state-of-the-art analysis. We do so via novel data poisoning attacks, which we show correspond to realistic privacy attacks. While previous work (Ma et al., arXiv 2019) proposed this connection between differential privacy and data poisoning as a defense against data poisoning, our use as a tool for understanding the privacy of a specific mechanism is new. More generally, our work takes a quantitative, empirical approach to understanding the privacy afforded by specific implementations of differentially private algorithms that we believe has the potential to complement and influence analytical work on differential privacy.

• The paper introduces novel data poisoning attacks to empirically audit DP-SGD's privacy, revealing gaps between theoretical and practical guarantees.
• The paper demonstrates a 10-fold improvement in estimating privacy lower bounds, bringing experimental results closer to worst-case theoretical limits.
• The paper analyzes the impact of gradient clipping and noise magnitude, offering actionable insights for balancing privacy and performance in DP-SGD.

Auditing Differentially Private SGD: Evaluation through Data Poisoning Attacks

The research paper presents an in-depth examination of the effectiveness of Differentially Private Stochastic Gradient Descent (DP-SGD) as a privacy-preserving mechanism. The investigation is conducted through experimental evaluations using innovative data-poisoning attacks, offering insights beyond traditional analytical methods. This empirical approach aims to assess whether DP-SGD provides stronger practical privacy guarantees than suggested by theoretical predictions.

Core Contributions

Empirical Evaluation of DP-SGD: The authors devised a novel methodology for auditing the DP-SGD algorithm by leveraging data poisoning attacks. By introducing adversarial perturbations into a dataset, the paper demonstrates how such attacks can reveal vulnerabilities in privacy guarantees. These perturbations enable a stronger differentiation between outputs from the original and poisoned datasets, indicating potential privacy breaches under DP-SGD.

Improved Attack Efficacy: The research effectively showcases the efficacy of new data poisoning strategies compared to previous methods. Notably, these attacks offer a 10-fold improvement in the lower bound estimation of privacy parameters over earlier techniques and present bounds significantly closer to the worst-case upper bounds previously derived from theoretical frameworks. Such results imply that the gap between practical privacy leakage and theoretical guarantees may not be as wide as previously thought.

Analysis of Parameters Influencing Privacy: The paper also explores how varying parameters such as gradient clipping and noise magnitude influence the privacy guarantees of DP-SGD. The findings suggest that factors such as initialization randomness and gradient norms play significant roles in actual privacy outcomes, offering practical insights for tuning these parameters to enhance privacy without compromising performance.

Implications for Privacy and Machine Learning

The paper's findings underline several critical implications for the field of differentially private machine learning:

1. Complementary Role of Empirical Evaluations: The paper reinforces the importance of empirical approaches, such as auditing via adversarial attacks, as complementary to theoretical analyses. Empirical evaluations can reveal nuanced insights about the real-world performance of privacy-preserving mechanisms like DP-SGD, which purely theoretical analyses might overlook.
2. Practical Considerations for DP-SGD Deployments: By highlighting the role of various hyperparameters in influencing privacy guarantees, the paper provides valuable guidance for practitioners. This guidance is crucial for deploying DP-SGD in real-world scenarios where balancing the trade-off between utility and privacy is essential.
3. Direction for Future Research: The work opens avenues for further exploration into tightening privacy bounds and understanding the implications of different hyperparameter configurations. Additionally, it prompts further research into the design of data poisoning schemes that can more accurately quantify and communicate privacy risks in practice.

In conclusion, this research paper offers a detailed and empirical perspective on the practical privacy offered by DP-SGD. By drawing connections between differential privacy and data poisoning attacks, it provides substantial evidence that can motivate both theoretical improvements and practical optimizations for differentially private machine learning frameworks. As such, it represents a significant step toward understanding and enhancing the practical privacy guarantees in the deployment of privacy-preserving machine learning models.