Google Researchers Present Novel Single-Run Approach to Auditing Differentially Private Machine Learning Systems

Differential privacy (DP) is a well-known technique in machine learning that aims to safeguard the privacy of people whose data is used to train models. It is a mathematical framework that ensures that the output of a model is not influenced by the presence or absence of any individual in the input data. Recently, a new auditing scheme has been developed that allows privacy guarantees in such models to be evaluated in a versatile and efficient way, with minimal assumptions about the underlying algorithm.

Google researchers present an auditing scheme for differentially private machine learning systems that focuses on a single training run. The study highlights the connection between PD and statistical generalization, a crucial aspect of the proposed audit approach.

DP ensures that individual data does not significantly affect results and provides a measurable privacy guarantee. Privacy audits evaluate analysis or implementation errors in DP algorithms. Conventional audits are computationally expensive and often require multiple executions. Leveraging parallelism by adding or removing training examples independently, the scheme imposes minimal assumptions on the algorithm and is adaptable to black-box and white-box scenarios.

The method, described in Algorithm 1 of the study, independently includes or excludes examples and computer scores for decision making. By analyzing the connection between PD and statistical generalization, the approach is applicable in black-box and white-box scenarios. Algorithm 3, DP-SGD Auditor, is a specific instantiation. It emphasizes the generic applicability of its audit methods to various differentially private algorithms, considering factors such as examples in distribution and evaluating different parameters.

The audit method offers a quantifiable privacy guarantee, helping to evaluate mathematical analysis or error detection. The generic applicability of the proposed audit methods to various differentially private algorithms is emphasized, with considerations for examples in distribution and parameter evaluations, demonstrating effective privacy guarantees with reduced computational costs.

The proposed auditing scheme allows evaluating differentially private machine learning techniques with a single training run, taking advantage of parallelism by adding or removing training examples independently. The approach demonstrates effective privacy guarantees with reduced computational costs compared to traditional audits. The generic nature of the audit methods is highlighted, which are suitable for different differentially private algorithms. It addresses practical considerations, such as the use of examples in distribution and parameter evaluations, making a valuable contribution to privacy auditing.

In conclusion, the key conclusions of the study can be summarized in a few points:

• The proposed auditing scheme allows the evaluation of differentially private machine learning techniques with a single training run, using parallelism by adding or removing training examples.
• The approach requires minimal assumptions about the algorithm and can be applied in both black-box and white-box environments.
• The scheme offers a quantifiable privacy guarantee and can detect errors in the algorithm implementation or evaluate the accuracy of mathematical analyses.
• It is suitable for various differentially private algorithms and provides effective privacy guarantees with reduced computational costs compared to traditional audits.

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