Differential privacy (DP) is a well-known technique in machine learning that aims to safeguard the privacy of individuals whose data is used to train models. It is a mathematical framework that guarantees 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 for the assessment of privacy guarantees in such models in a versatile and efficient manner, with minimal assumptions on the underlying algorithm.
Google Researchers introduce an auditing scheme for differentially private machine learning systems focusing on a single training run. The study highlights the connection between DP and statistical generalization, a crucial aspect of the proposed auditing approach.
DP ensures that individual data doesn’t significantly impact outcomes, offering a quantifiable privacy guarantee. Privacy audits evaluate analysis or implementation errors in DP algorithms. Conventional audits are computationally expensive, often requiring multiple runs. Leveraging parallelism in independently adding or removing training examples, the scheme imposes minimal assumptions on the algorithm and is adaptable to black-box and white-box scenarios.
The method, outlined in Algorithm 1 in the study, independently includes or excludes examples and computing scores for decision-making. Analyzing the connection between DP 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 their auditing methods to various differentially private algorithms, considering factors like in-distribution examples and evaluating different parameters.
The auditing method offers a quantifiable privacy guarantee, aiding in assessing mathematical analyses or error detection. The generic applicability of the proposed auditing methods to various differentially private algorithms is emphasized, with considerations for in-distribution examples and parameter evaluations, demonstrating effective privacy guarantees with reduced computational costs.
The proposed auditing scheme allows for assessing differentially private machine learning techniques with a single training run, leveraging parallelism in independently adding or removing training examples. The approach demonstrates effective privacy guarantees with reduced computational costs compared to traditional audits. The generic nature of the auditing methods, which are suitable for various differentially private algorithms, is highlighted. It addresses practical considerations, such as using in-distribution examples and parameter evaluations, making a valuable contribution to privacy auditing.
In conclusion, the study’s key takeaways can be summarized in a few points:
- The proposed auditing scheme enables the evaluation of differentially private machine learning techniques with a single training run, using parallelism in adding or removing training examples
- The approach requires minimal assumptions about the algorithm and can be applied in both black-box and white-box settings
- The scheme offers a quantifiable privacy guarantee and can detect errors in algorithm implementation or assess 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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