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Found 12 papers, 1 papers with code
Training Differentially Private Models with Secure Multiparty Computation
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 protection of each owner's data.
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.
Privacy-Preserving Feature Selection with Secure Multiparty Computation
In this work, we propose the first MPC based protocol for private feature selection based on the filter method, which is independent of model training, and can be used in combination with any MPC protocol to rank features.
Privacy-Preserving Video Classification with Convolutional Neural Networks
We propose a privacy-preserving implementation of single-frame method based video classification with convolutional neural networks that allows a party to infer a label from a video without necessitating the video owner to disclose their video to other entities in an unencrypted manner.
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.
Private Speech Classification with Secure Multiparty Computation
We evaluate the efficiency-security-accuracy trade-off of the proposed solution in a use case for privacy-preserving emotion detection from speech with a convolutional neural network.
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.
Privacy-Preserving Classification of Personal Text Messages with Secure Multi-Party Computation
Classification of personal text messages has many useful applications in surveillance, e-commerce, and mental health care, to name a few.
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.
