1 citations · 1 across the 9 of their papers we have counts for
15 papers
HXAI: Hierarchical Privacy-Preserving Explainable AI in Distributed Energy Systems
Poushali Sengupta, Sabita Maharjan, Frank Eliassen +1
Balancing electricity demand and supply is increasingly difficult due to the inherent intermittency of renewable power generation and the stochastic power consumption. Grid operato…
MCIR: A Feature Dependence-Aware Explainability Method with Reliability Guarantees
Poushali Sengupta, Sabita Maharjan, Frank Eliassen +2
Modern machine-learning models often contain strongly dependent or redundant features, making feature attribution difficult because shared predictive information can be distributed…
X-NegoBox: An Explainable Privacy-Budget Negotiation Framework for Secure Peer-to-Peer Energy Data Exchange
Poushali Sengupta, Sabita Maharjan, Frank Eliassen +1
The decentralization of modern energy systems is transforming consumers into prosumers who continuously exchange data with aggregators, peers, and market operators. While such data…
Reliable Explanations or Random Noise? A Reliability Metric for XAI
Poushali Sengupta, Sabita Maharjan, Frank Eliassen +2
In recent years, explaining decisions made by complex machine learning models has become essential in high-stakes domains such as energy systems, healthcare, finance, and autonomou…
Context Dependence and Reliability in Autoregressive Language Models
Poushali Sengupta, Shashi Raj Pandey, Sabita Maharjan +1
Large language models (LLMs) generate outputs by utilizing extensive context, which often includes redundant information from prompts, retrieved passages, and interaction history.…
Adaptive Quantum-Safe Cryptography for 6G Vehicular Networks via Context-Aware Optimization
Poushali Sengupta, Mayank Raikwar, Sabita Maharjan +2
Powerful quantum computers in the future may be able to break the security used for communication between vehicles and other devices (Vehicle-to-Everything, or V2X). New security m…
Explainability of Complex AI Models with Correlation Impact Ratio
Poushali Sengupta, Rabindra Khadka, Sabita Maharjan +5
Complex AI systems make better predictions but often lack transparency, limiting trustworthiness, interpretability, and safe deployment. Common post hoc AI explainers, such as LIME…
Correlation-Aware Feature Attribution Based Explainable AI
Poushali Sengupta, Yan Zhang, Frank Eliassen +1
Explainable AI (XAI) is increasingly essential as modern models become more complex and high-stakes applications demand transparency, trust, and regulatory compliance. Existing glo…
Privacy-Utility-Fairness: A Balanced Approach to Vehicular-Traffic Management System
Poushali Sengupta, Sabita Maharjan, frank Eliassen +1
Location-based vehicular traffic management faces significant challenges in protecting sensitive geographical data while maintaining utility for traffic management and fairness acr…
Privacy-preserving transactive energy systems: Key topics and open research challenges
Daniel Gerbi Duguma, Juliana Zhang, Meysam Aboutalebi +20
This manuscript aims to formalize and conclude the discussions initiated during the PriTEM workshop 22-23 March 2023. We present important ideas and discussion topics in the contex…
Balancing Explainability-Accuracy of Complex Models
Poushali Sengupta, Yan Zhang, Sabita Maharjan +1
Explainability of AI models is an important topic that can have a significant impact in all domains and applications from autonomous driving to healthcare. The existing approaches…
Fairly Private Through Group Tagging and Relation Impact
Poushali Sengupta, Subhankar Mishra
Privacy and Fairness both are very important nowadays. For most of the cases in the online service providing system, users have to share their personal information with the organiz…
FLaPS: Federated Learning and Privately Scaling
Sudipta Paul, Poushali Sengupta, Subhankar Mishra
Federated learning (FL) is a distributed learning process where the model (weights and checkpoints) is transferred to the devices that posses data rather than the classical way of…
Learning With Differential Privacy
Poushali Sengupta, Sudipta Paul, Subhankar Mishra
The leakage of data might have been an extreme effect on the personal level if it contains sensitive information. Common prevention methods like encryption-decryption, endpoint pro…
BUDS: Balancing Utility and Differential Privacy by Shuffling
Poushali Sengupta, Sudipta Paul, Subhankar Mishra
Balancing utility and differential privacy by shuffling or \textit{BUDS} is an approach towards crowd-sourced, statistical databases, with strong privacy and utility balance using…