As data privacy increasingly becomes a critical societal concern, federated learning has
been a hot research topic in enabling the collaborative training of machine learning models …

Nowadays, data privacy is an important consideration in machine learning. This paper
provides an overview of how Federated Learning can be used to improve data security and …

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 …

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 …

We present Chameleon, a novel hybrid (mixed-protocol) framework for secure function
evaluation (SFE) which enables two parties to jointly compute a function without disclosing …

Federated learning has recently emerged as a paradigm promising the benefits of
harnessing rich data from diverse sources to train high quality models, with the salient …

We present CrypTFlow, a first of its kind system that converts TensorFlow inference code into
Secure Multi-party Computation (MPC) protocols at the push of a button. To do this, we build …

Functional encryption (FE) is an extremely powerful cryptographic mechanism that lets an
authorized entity compute on encrypted data, and learn the results in the clear. However, all …

Intel's Software Guard Extensions (SGX) promises an isolated execution environment,
protected from all software running on the machine. A significant limitation of SGX is its lack …

This chapter focuses on describing how the human‐centered design (HCD) process can be
revisited and expanded in an artificial intelligence (AI) context, proposing a methodological …