We first examine different privacy risks during decision tree based inductive learning processes, including the sensitivity of private input data and potential ...
This paper revisits the concepts and techniques of privacy-preserving decision tree learning, a fundamental model of inductive learning. We first examine ...
We first examine different privacy risks during decision tree based inductive learning processes, including the sensitivity of private input data and potential ...
To address this concern, privacy-preserving techniques mostly employ one of 4 approaches: Homomorphic Encryption (HE) [7][8][9][10], Multiparty Computation ...
May 19, 2022 · In this work we focus on the ever-popular tree based methods, and propose a new privacy-preserving solution to training and prediction for trees ...
Abstract. Privacy-preserving machine learning enables secure outsourc- ing of machine learning tasks to an untrusted service provider (server).
Missing: Inductive | Show results with:Inductive
The decision-tree-classification model is one of the important classifications used in this field. Existing work uses bit encryption, and the communication cost ...
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We address the problem of decision tree learning from data that may be distributed across multiple data owners while protecting the privacy of the training data ...
The learning. (or induction) process of a tree determines the splitting feature and split point for every tree node and results in what is known as the tree's ...
Privacy-Preserving Inductive Learning with Decision Trees. S. Truex, L. Liu, M. Gursoy, and L. Yu. BigData Congress, page 57-64. IEEE Computer Society ...