ML Privacy Meter: Aiding Regulatory Compliance by Quantifying the Privacy Risks of Machine LearningThanks: Repository for the code and tutorials is available at https://github.com/privacytrustlab/ml_privacy_meter

摘要

When building machine learning models using sensitive data, organizations should ensure that the data processed in such systems is adequately protected. For projects involving machine learning on personal data, Article 35 of the GDPR mandates it to perform a Data Protection Impact Assessment (DPIA). In addition to the threats of illegitimate access to data through security breaches, machine learning models pose an additional privacy risk to the data by indirectly revealing about it through the model predictions and parameters. Guidances released by the Information Commissioner’s Office (UK) and the National Institute of Standards and Technology (US) emphasize on the threats to data from models and recommend organizations to account for and estimate these risks to comply with data protection regulations. Hence, there is an immediate need for a tool that can quantify the privacy risks to data from models.