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5 papers
Inferring Sensitive Attributes from Model Explanations
Vasisht Duddu, Antoine Boutet
Model explanations provide transparency into a trained machine learning model's blackbox behavior to a model builder. They indicate the influence of different input attributes to i…
Quantifying Privacy Leakage in Graph Embedding
Vasisht Duddu, Antoine Boutet, Virat Shejwalkar
Graph embeddings have been proposed to map graph data to low dimensional space for downstream processing (e.g., node classification or link prediction). With the increasing collect…
Quantifying (Hyper) Parameter Leakage in Machine Learning
Vasisht Duddu, D. Vijay Rao
Machine Learning models, extensively used for various multimedia applications, are offered to users as a blackbox service on the Cloud on a pay-per-query basis. Such blackbox model…
Fault Tolerance of Neural Networks in Adversarial Settings
Vasisht Duddu, N. Rajesh Pillai, D. Vijay Rao +1
Artificial Intelligence systems require a through assessment of different pillars of trust, namely, fairness, interpretability, data and model privacy, reliability (safety) and rob…
Stealing Neural Networks via Timing Side Channels
Vasisht Duddu, Debasis Samanta, D Vijay Rao +1
Deep learning is gaining importance in many applications. However, Neural Networks face several security and privacy threats. This is particularly significant in the scenario where…