Releasing representative data sets without compromising the data privacy has attracted
increasing attention from the database community in recent years. Differential privacy is an …

Driven by technologies such as mobile edge computing and 5G, recent years have
witnessed the rapid development of urban informatics, where a large amount of data is …

Recent success of deep neural networks (DNNs) hinges on the availability of large-scale
dataset; however, training on such dataset often poses privacy risks for sensitive training …

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 …

Iterative algorithms, like gradient descent, are common tools for solving a variety of
problems, such as model fitting. For this reason, there is interest in creating differentially …

Many real-world networks are inherently decentralized. For example, in social networks,
each user maintains a local view of a social graph, such as a list of friends and her profile. It …

Differential privacy (DP) is a widely accepted mathematical framework for protecting data
privacy. Simply stated, it guarantees that the distribution of query results changes only …

Differential privacy (DP) has been widely explored in academia recently but less so in
industry possibly due to its strong privacy guarantee. This paper makes the first attempt to …

Abstract Differentially Private-SGD (DP-SGD) of Abadi et al. and its variations are the only
known algorithms for private training of large scale neural networks. This algorithm requires …

Federated recommendation can potentially alleviate the privacy concerns in collecting
sensitive and personal data for training personalized recommendation systems. However, it …