1 citations · 1 across the 6 of their papers we have counts for
6 papers
TADP-RME: A Trust-Adaptive Differential Privacy Framework for Enhancing Reliability of Data-Driven Systems
Labani Halder, Payel Sadhukhan, Sarbani Palit
Ensuring reliability in adversarial settings necessitates treating privacy as a foundational component of data-driven systems. While differential privacy and cryptographic protocol…
Demystifying the trend of the healthcare index: Is historical price a key driver?
Payel Sadhukhan, Samrat Gupta, Subhasis Ghosh +1
Healthcare sector indices consolidate the economic health of pharmaceutical, biotechnology, and healthcare service firms. The short-term movements in these indices are closely inte…
Footprints of Data in a Classifier: Understanding the Privacy Risks and Solution Strategies
Payel Sadhukhan, Tanujit Chakraborty
The widespread deployment of Artificial Intelligence (AI) across government and private industries brings both advancements and heightened privacy and security concerns. Article 17…
Reconnoitering the class distinguishing abilities of the features, to know them better
Payel Sadhukhan, Sarbani palit, Kausik Sengupta
The relevance of machine learning (ML) in our daily lives is closely intertwined with its explainability. Explainability can allow end-users to have a transparent and humane reckon…
Random Walk-steered Majority Undersampling
Payel Sadhukhan, Arjun Pakrashi, Brian Mac Namee
In this work, we propose Random Walk-steered Majority Undersampling (RWMaU), which undersamples the majority points of a class imbalanced dataset, in order to balance the classes.…
Integrating Unsupervised Clustering and Label-specific Oversampling to Tackle Imbalanced Multi-label Data
Payel Sadhukhan, Arjun Pakrashi, Sarbani Palit +1
There is often a mixture of very frequent labels and very infrequent labels in multi-label datatsets. This variation in label frequency, a type class imbalance, creates a significa…