When a decision tree based classifier is hosted as a service in a critical application with the need for privacy protection of the service as well as the user ...
In this work, we turn our attention to the issue of de- ploying these ubiquitously used decision trees, hosted as a service (in a cloud environment), for ...
In this paper, we describe an end-to-end approach to support privacyenhanced decision tree classification using IBM supported open-source library HELib.
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In this paper, we describe an end-to-end approach to support privacy- enhanced decision tree classification using IBM supported open-source library HELib. Using ...
In this paper, we propose BloomDT, a privacy-preserving scheme for decision tree inference, which uses Bloom filters to hide the original decision tree's ...
These unwanted inferences are related to the users' identity, current location and other personal information. We have previously introduced 'inference ...
Abstract. We address the problem of decision tree learning from data that may be distributed across multiple data owners while protecting the privacy of the ...
Privacy enhancing technologies (PETs) have been proposed as a way to protect the privacy of data while still allowing for data analysis. In this work, we focus ...
Researchers have developed privacy-preserving approaches for DT training and inference using cryptographic primitives, such as Secure Multi-Party ... [Show full ...
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Abstract. Privacy-preserving decision tree evaluation (PDTE) allows a client that holds feature vectors to perform inferences against a de-.