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Code and data accompanying the paper: Quantifying Privacy Risks in Synthetic ...
This repository contains the code and data for the paper “Quantifying Privacy Risks in Synthetic Data: A Study on Black-Box Membership Inference”. It enables full... -
Machine Learning Explainability Via Microaggregation and Shallow Decision Trees
Artificial intelligence (AI) is being deployed in missions that are increasingly critical for human life. To build trust in AI and avoid an algorithm-based authoritarian... -
Explanation of Deep Models with Limited Interaction for Trade Secret and Priv...
An ever-increasing number of decisions affecting our lives are made by algorithms. For this reason, algorithmic transparency is becoming a pressing need: automated decisions... -
Machine Learning Explainability Through Comprehensible Decision Trees
The role of decisions made by machine learning algorithms in our lives is ever increasing. In reaction to this phenomenon, the European General Data Protection Regulation...
