Differential privacy (DP) in machine learning safeguards individuals’ data privacy by ensuring model outputs are not influenced by individual data. Google researchers introduced an auditing scheme for assessing privacy guarantees, emphasizing the connection between DP and statistical generalization. The scheme offers quantifiable privacy guarantees with reduced computational costs, suitable for various DP algorithms. [49 words]
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Differential Privacy Auditing for Machine Learning Systems
Overview
Differential privacy (DP) is a technique in machine learning that protects individuals’ privacy in data used for training models. A new auditing scheme has been developed to assess privacy guarantees in DP models efficiently and with minimal assumptions on the underlying algorithm.
Key Points
- The auditing scheme allows for evaluating 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.
- It offers a quantifiable privacy guarantee and can detect errors in algorithm implementation or assess the accuracy of mathematical analyses.
- The scheme is suitable for various differentially private algorithms and provides effective privacy guarantees with reduced computational costs compared to traditional audits.
Practical Applications
The proposed auditing scheme offers practical solutions for assessing privacy guarantees in differentially private machine learning systems. It enables efficient evaluation with reduced computational costs and minimal assumptions on the underlying algorithm.
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