Creating a Generic Adversarial Attack for any Synthetic Dataset
Can the privacy of a generated dataset be assessed through downstream adversarial attacks to highlight the risk of re-identification
An extensible code was developed to apply a suite of adversarial attacks to synthetically generated single table tabular data in order to assess the likely success of attacks and act as a privacy indicator for the dataset. Using this information then informs the generation and information governance process to ensure the safety of our data.
Results
The code-base was successfully developed with code injection points for extensibility. Unfortunately, as the code could be used as an active attack on a dataset, we have decided not not to make the codebase public but instead aiming to both extend the number of attacks and incorporate the code in our synthetic generation process.
Output | Link |
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Open Source Code & Documentation | restricted |
Case Study | Awaiting Sign-Off |
Technical report | Blod |