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Found 5 papers, 1 papers with code
Privacy-preserving transactive energy systems: Key topics and open research challenges
This manuscript aims to formalize and conclude the discussions initiated during the PriTEM workshop 22-23 March 2023.
Balancing Explainability-Accuracy of Complex Models
Furthermore, we provide an upper bound of the computation complexity of our proposed approach for the dependent features.
Autonomous Driving
Explainable Artificial Intelligence (XAI)
FLaPS: Federated Learning and Privately Scaling
FL uses the FedAvg algorithm, which is trained in the iterative model averaging way, on the non-iid and unbalanced distributed data, without depending on the data quantity.
Learning With Differential Privacy
The different aspects of differential privacy, it's application in privacy protection and leakage of information, a comparative discussion, on the current research approaches in this field, its utility in the real world as well as the trade-offs - will be discussed.
BUDS: Balancing Utility and Differential Privacy by Shuffling
In this work, after collecting one-hot encoded data from different sources and clients, a step of novel attribute shuffling technique using iterative shuffling (based on the query asked by the analyst) and loss estimation with an updation function and risk minimization produces a utility and privacy balanced differential private report.
