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11/18/2022
Leveraging Algorithmic Fairness to Mitigate Blackbox Attribute Inference Attacks
Machine learning (ML) models have been deployed for high-stakes applicat...
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08/21/2022
Inferring Sensitive Attributes from Model Explanations
Model explanations provide transparency into a trained machine learning ...
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08/17/2022
I-GWAS: Privacy-Preserving Interdependent Genome-Wide Association Studies
Genome-wide Association Studies (GWASes) identify genomic variations tha...
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02/04/2022
Dikaios: Privacy Auditing of Algorithmic Fairness via Attribute Inference Attacks
Machine learning (ML) models have been deployed for high-stakes applicat...
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09/26/2021
MixNN: Protection of Federated Learning Against Inference Attacks by Mixing Neural Network Layers
Machine Learning (ML) has emerged as a core technology to provide learni...
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06/15/2021
Privacy Assessment of Federated Learning using Private Personalized Layers
Federated Learning (FL) is a collaborative scheme to train a learning mo...
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10/02/2020
GECKO: Reconciling Privacy, Accuracy and Efficiency in Embedded Deep Learning
Embedded systems demand on-device processing of data using Neural Networ...
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10/02/2020
Quantifying Privacy Leakage in Graph Embedding
Graph embeddings have been proposed to map graph data to low dimensional...
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08/04/2020
DESIRE: A Third Way for a European Exposure Notification System Leveraging the best of centralized and decentralized systems
This document presents an evolution of the ROBERT protocol that decentra...
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03/23/2020
DYSAN: Dynamically sanitizing motion sensor data against sensitive inferences through adversarial networks
With the widespread adoption of the quantified self movement, an increas...
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05/04/2018
X-Search: Revisiting Private Web Search using Intel SGX
The exploitation of user search queries by search engines is at the hear...
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05/03/2018
