Sharding is the prevalent approach to breaking the trilemma of simultaneously achieving
decentralization, security, and scalability in traditional blockchain systems, which are …

Today's artificial intelligence still faces two major challenges. One is that, in most industries,
data exists in the form of isolated islands. The other is the strengthening of data privacy and …

In recent years, the privacy issue in Vehicular Edge Computing (VEC) has gained a lot of
concern. The privacy problem is even more severe in autonomous driving business than the …

Federated learning (FL) is an emerging paradigm that enables multiple organizations to
jointly train a model without revealing their private data to each other. This paper studies {\it …

With the proliferation of social sensing, large amounts of observation are contributed by
people or devices. However, these observations contain disinformation. Disinformation can …

Secure multiparty computation (MPC) often relies on correlated randomness for better
efficiency and simplicity. This is particularly useful for MPC with no honest majority, where …

Federated learning (FL) has been proposed to protect data privacy and virtually assemble
the isolated data silos by cooperatively training models among organizations without …

We propose a new notion of secure multiparty computation aided by a computationally-
powerful but untrusted" cloud" server. In this notion that we call on-the-fly multiparty …

We construct a general multiparty computation (MPC) protocol with only two rounds of
interaction in the common random string model, which is known to be optimal. In the honest …

We propose the first fully homomorphic encryption scheme, solving an old open problem.
Such a scheme allows one to compute arbitrary functions over encrypted data without the …