Harsh Kasyap
YOU?
Author Swipe
View article: Fairness-Constrained Optimization Attack in Federated Learning
Fairness-Constrained Optimization Attack in Federated Learning Open
Federated learning (FL) is a privacy-preserving machine learning technique that facilitates collaboration among participants across demographics. FL enables model sharing, while restricting the movement of data. Since FL provides participa…
View article: Mitigating Bias: Model Pruning for Enhanced Model Fairness and Efficiency
Mitigating Bias: Model Pruning for Enhanced Model Fairness and Efficiency Open
Machine learning models have been instrumental in making decisions across domains, like mortgage lending and risk assessment in finance. However, these models have been found susceptible to biases, causing unfair decisions for a specific g…
View article: Privacy-preserving Fuzzy Name Matching for Sharing Financial Intelligence
Privacy-preserving Fuzzy Name Matching for Sharing Financial Intelligence Open
Financial institutions rely on data for many operations, including a need to drive efficiency, enhance services and prevent financial crime. Data sharing across an organisation or between institutions can facilitate rapid, evidence-based d…
View article: Review of: "Secure and Private Machine Learning: A Survey of Techniques and Applications"
Review of: "Secure and Private Machine Learning: A Survey of Techniques and Applications" Open
View article: Privacy-Preserving Federated Learning Framework using Permissioned Blockchain
Privacy-Preserving Federated Learning Framework using Permissioned Blockchain Open
Data is readily available with the growing number of smart and IoT devices. Industries of different sectors follow technological advancement to be benefited from data sharing. However, application-specific data is available in small chunks…
View article: Beyond Data Poisoning in Federated Learning
Beyond Data Poisoning in Federated Learning Open