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Found 7 papers, 1 papers with code
Privacy-Preserving Training of Tree Ensembles over Continuous Data
In this paper we propose three more efficient alternatives for secure training of decision tree based models on data with continuous features, namely: (1) secure discretization of the data, followed by secure training of a decision tree over the discretized data; (2) secure discretization of the data, followed by secure training of a random forest over the discretized data; and (3) secure training of extremely randomized trees (``extra-trees'') on the original data.
Round and Communication Balanced Protocols for Oblivious Evaluation of Finite State Machines
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 string in such a manner that neither party learns the other's input, and the states being visited are hidden from both.
Fast Privacy-Preserving Text Classification based on Secure Multiparty Computation
We propose a privacy-preserving Naive Bayes classifier and apply it to the problem of private text classification.
High Performance Logistic Regression for Privacy-Preserving Genome Analysis
In this paper, we present a secure logistic regression training protocol and its implementation, with a new subprotocol to securely compute the activation function.
Protecting Privacy of Users in Brain-Computer Interface Applications
Machine learning (ML) is revolutionizing research and industry.
Privacy-Preserving Classification of Personal Text Messages with Secure Multi-Party Computation: An Application to Hate-Speech Detection
Classification of personal text messages has many useful applications in surveillance, e-commerce, and mental health care, to name a few.
VirtualIdentity: Privacy-Preserving User Profiling
User profiling from user generated content (UGC) is a common practice that supports the business models of many social media companies.
