In this paper, we apply machine learning to distributed private data owned by multiple data
owners, entities with access to non-overlapping training datasets. We use noisy …

With the amount of user data crowdsourced for data mining dramatically increasing, there is
an urgent need to protect the privacy of individuals. Differential privacy mechanisms are …

Users' privacy is vulnerable at all stages of the deep learning process. Sensitive information
of users may be disclosed during data collection, during training, or even after releasing the …

Distributed learning allows a group of independent data owners to collaboratively learn a
model over their data sets without exposing their private data. We present a distributed …

Deployment of deep learning in different fields and industries is growing day by day due to
its performance, which relies on the availability of data and compute. Data is often crowd …

We detail a new framework for privacy preserving deep learning and discuss its assets. The
framework puts a premium on ownership and secure processing of data and introduces a …

Differentially private (DP) machine learning allows us to train models on private data while
limiting data leakage. DP formalizes this data leakage through a cryptographic game, where …

Privacy preserving machine learning algorithms are crucial for learning models over user
data to protect sensitive information. Motivated by this, differentially private stochastic …

The objective of machine learning is to extract useful information from data, while privacy is
preserved by concealing information. Thus it seems hard to reconcile these competing …

Machine learning techniques based on neural networks are achieving remarkable results in
a wide variety of domains. Often, the training of models requires large, representative …