Machine learning (ML) is increasingly being adopted in a wide variety of application
domains. Usually, a well-performing ML model relies on a large volume of training data and …

The rapid development of cloud computing promotes a wide deployment of data and
computation outsourcing to cloud service providers by resource-limited entities. Based on a …

Secure multi-party computation (MPC) has evolved from a theoretical curiosity in the 1980s
to a tool for building real systems today. Over the past decade, MPC has been one of the …

Privacy-preserving multi-party machine learning allows multiple organizations to perform
collaborative data analytics while guaranteeing the privacy of their individual datasets …

We study the problem of dynamic symmetric searchable encryption. In that setting, it is
crucial to minimize the information revealed to the server as a result of update operations …

We present Path ORAM, an extremely simple Oblivious RAM protocol with a small amount of
client storage. Partly due to its simplicity, Path ORAM is the most practical ORAM scheme …

We design and develop ObliVM, a programming framework for secure computation. ObliVM
offers a domain specific language designed for compilation of programs into efficient …

We are witnessing a confluence between applied cryptography and secure hardware
systems in enabling secure cloud computing. On one hand, work in applied cryptography …

Search indices are fundamental building blocks of many systems, and there is great interest
in running them on encrypted data. Unfortunately, many known schemes that enable search …

Searchable symmetric encryption (SSE) has been widely applied in the encrypted database
for queries in practice. Although SSE is powerful and feature-rich, it is always plagued by …