Publication Type

Journal Article

Version

submittedVersion

Publication Date

11-2023

Abstract

With the rising awareness of data assets, data governance, which is to understand where data comes from, how it is collected, and how it is used, has been assuming evergrowing importance. One critical component of data governance gaining increasing attention is auditing machine learning models to determine if specific data has been used for training. Existing auditing techniques, like shadow auditing methods, have shown feasibility under specific conditions such as having access to label information and knowledge of training protocols. However, these conditions are often not met in most real-world applications. In this paper, we introduce a practical framework for auditing data provenance based on a differential mechanism, i.e., after carefully designed transformation, perturbed input data from the target model's training set would result in much more drastic changes in the output than those from the model's non-training set. Our framework is data-dependent and does not require distinguishing training data from non-training data or training additional shadow models with labeled output data. Furthermore, our framework extends beyond point-based data auditing to group-based data auditing, aligning with the needs of real-world applications. Our theoretical analysis of the differential mechanism and the experimental results on real-world data sets verify the proposal's effectiveness. The codes have been uploaded in an anonymous link.

Keywords

Data models, training data, biological system modeling, computational modeling, predictive models, machine learning

Discipline

Databases and Information Systems | Data Storage Systems | Numerical Analysis and Scientific Computing

Research Areas

Data Science and Engineering

Publication

IEEE Transactions on Knowledge and Data Engineering

First Page

1

Last Page

12

ISSN

1041-4347

Identifier

10.1109/TKDE.2023.3334821

Publisher

IEEE

Copyright Owner and License

Authors

Creative Commons License

Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.

Additional URL

https://doi.org/10.1109/TKDE.2023.3334821

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