Quantum Privacy Aggregation of Teacher Ensembles (QPATE) for Privacy-preserving Quantum Machine Learning
The utility of machine learning has rapidly expanded in the last two decades and presents an ethical challenge. Papernot et. al. developed a technique, known as Private Aggregation of Teacher Ensembles (PATE) to enable federated learning in which multiple teacher models are trained on disjoint datasets. This study is the first to apply PATE to an ensemble of quantum neural networks (QNN) to pave a new way of ensuring privacy in quantum machine learning (QML) models.
Code (0)
등록된 구현이 없습니다.
Tasks
Federated LearningPrivacy PreservingQuantum Machine LearningSimilar Papers 제목 키워드 기반
SeqPATE: Differentially Private Text Generation via Knowledge Distillation
Protecting the privacy of user data is crucial when training neural text generation models, which may leak sensitive user information during generation. Differentially private (DP) learning algorithms provide guarantees …
Knowledge DistillationSentenceSentence CompletionText GenerationScalable Differentially Private Data Generation via Private Aggregation of Teacher Ensembles
We present a novel approach named G-PATE for training differentially private data generator. The generator can be used to produce synthetic datasets with strong privacy guarantee while preserving high data utility. Our a…
A Fairness Analysis on Private Aggregation of Teacher Ensembles
The Private Aggregation of Teacher Ensembles (PATE) is an important private machine learning framework. It combines multiple learning models used as teachers for a student model that learns to predict an output chosen by…
FairnessPrivacy PreservingPATE-AAE: Incorporating Adversarial Autoencoder into Private Aggregation of Teacher Ensembles for Spoken Command Classification
We propose using an adversarial autoencoder (AAE) to replace generative adversarial network (GAN) in the private aggregation of teacher ensembles (PATE), a solution for ensuring differential privacy in speech application…
Generative Adversarial NetworkKeyword SpottingPrivacy PreservingHot PATE: Private Aggregation of Distributions for Diverse Task
The Private Aggregation of Teacher Ensembles (PATE) framework enables privacy-preserving machine learning by aggregating responses from disjoint subsets of sensitive data. Adaptations of PATE to tasks with inherent outpu…
DiversityIn-Context LearningPrivacy PreservingText Generation+2