483 citations · 555 across the 13 of their papers we have counts for
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Investigating Bias in Image Classification using Model Explanations
Schrasing Tong, Lalana Kagal
We evaluated whether model explanations could efficiently detect bias in image classification by highlighting discriminating features, thereby removing the reliance on sensitive at…
DPD-InfoGAN: Differentially Private Distributed InfoGAN
Vaikkunth Mugunthan, Vignesh Gokul, Lalana Kagal +1
Generative Adversarial Networks (GANs) are deep learning architectures capable of generating synthetic datasets. Despite producing high-quality synthetic images, the default GAN ha…
BlockFLow: An Accountable and Privacy-Preserving Solution for Federated Learning
Vaikkunth Mugunthan, Ravi Rahman, Lalana Kagal
Federated learning enables the development of a machine learning model among collaborating agents without requiring them to share their underlying data. However, malicious agents w…
PrivacyFL: A simulator for privacy-preserving and secure federated learning
Vaikkunth Mugunthan, Anton Peraire-Bueno, Lalana Kagal
Federated learning is a technique that enables distributed clients to collaboratively learn a shared machine learning model while keeping their training data localized. This reduce…
Dark Patterns after the GDPR: Scraping Consent Pop-ups and Demonstrating their Influence
Midas Nouwens, Ilaria Liccardi, Michael Veale +2
New consent management platforms (CMPs) have been introduced to the web to conform with the EU's General Data Protection Regulation, particularly its requirements for consent when…