Ensuring differential privacy of models learned from sensitive user data is an important goal that has been studied extensively in recent years.
Mar 27, 2018
Abstract. Ensuring differential privacy of models learned from sensitive user data is an important goal that has been studied extensively in recent years.
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Ensuring differential privacy of models learned from sensitive user data is an important goal that has been studied extensively in recent years.
In privacy-preserving machine learning, individual parties are reluctant to share their sensitive training data due to privacy concerns.
Oct 27, 2024 · Synthetic data have been recognized as a promising solution, coupling privacy-preservation with sufficient quality for analysis. Generated by ...
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Feb 11, 2020 · To create privacy-preserving prediction models, our tool implements supervised learning from anonymized data. To maximize the performance of the ...
Highlights · Privacy-preserving neural network prediction with multi-client functional encryption. · Secure prediction under multiple separated data providers.
Jul 5, 2024 · This study explores whether using HE to integrate encrypted multi-institutional data enhances predictive power in research.
Dec 6, 2023 · Abstract:A User Next Location Prediction (UNLP) task, which predicts the next location that a user will move to given his/her trajectory, ...
In privacy-preserving machine learning, individual parties are reluctant to share their sensitive training data due to privacy concerns.