Huge amounts of unstructured data including image, video, audio, and text are ubiquitously
generated and shared, and it is a challenge to protect sensitive personal information in …

Digitization blurs the lines between technology and management, facilitating new business
models built upon the concepts, methods and tools of the digital environment. The purpose …

Federated learning (FL) is a machine learning setting where many clients (eg, mobile
devices or whole organizations) collaboratively train a model under the orchestration of a …

As data are increasingly being stored in different silos and societies becoming more aware
of data privacy issues, the traditional centralized training of artificial intelligence (AI) models …

Federated learning facilitates the collaborative training of models without the sharing of raw
data. However, recent attacks demonstrate that simply maintaining data locality during …

Modern cyber physical systems (CPSs) has widely being used in our daily lives because of
development of information and communication technologies (ICT). With the provision of …

We design a novel, communication-efficient, failure-robust protocol for secure aggregation of
high-dimensional data. Our protocol allows a server to compute the sum of large, user-held …

Federated learning is a distributed framework for training machine learning models over the
data residing at mobile devices, while protecting the privacy of individual users. A major …

Secure Aggregation protocols allow a collection of mutually distrust parties, each holding a
private value, to collaboratively compute the sum of those values without revealing the …

Big data has potential to unlock novel groundbreaking opportunities in power grid that
enhances a multitude of technical, social, and economic gains. As power grid technologies …