20 citations · 57 across the 13 of their papers we have counts for
19 papers
Large Language Models for Cryptocurrency Transaction Analysis: A Bitcoin Case Study
Yuchen Lei, Yuexin Xiang, Qin Wang +5
Cryptocurrencies are widely used, yet current methods for analyzing transactions often rely on opaque, black-box models. While these models may achieve high performance, their outp…
SePEnTra: A secure and privacy-preserving energy trading mechanisms in transactive energy market
Rumpa Dasgupta, Amin Sakzad, Carsten Rudolph +1
In this paper, we design and present a novel model called SePEnTra to ensure the security and privacy of energy data while sharing with other entities during energy trading to dete…
Training Differentially Private Models with Secure Multiparty Computation
Sikha Pentyala, Davis Railsback, Ricardo Maia +4
We address the problem of learning a machine learning model from training data that originates at multiple data owners while providing formal privacy guarantees regarding the prote…
Privacy-Preserving Training of Tree Ensembles over Continuous Data
Samuel Adams, Chaitali Choudhary, Martine De Cock +5
Most existing Secure Multi-Party Computation (MPC) protocols for privacy-preserving training of decision trees over distributed data assume that the features are categorical. In re…
Round and Communication Balanced Protocols for Oblivious Evaluation of Finite State Machines
Rafael Dowsley, Caleb Horst, Anderson C. A. Nascimento
We propose protocols for obliviously evaluating finite-state machines, i.e., the evaluation is shared between the provider of the finite-state machine and the provider of the input…
Privacy-Preserving Feature Selection with Secure Multiparty Computation
Xiling Li, Rafael Dowsley, Martine De Cock
Existing work on privacy-preserving machine learning with Secure Multiparty Computation (MPC) is almost exclusively focused on model training and on inference with trained models,…