Secure multi-party computation (MPC) allows parties to perform computations on data while
keeping that data private. This capability has great potential for machine-learning …

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 …

Applications of Blockchain (BC) technology and Cyber-Physical Systems (CPS) are
increasing exponentially. However, framing resilient and correct smart contracts (SCs) for …

Abstract Recently, The Internet of Vehicles (IoV) concept is becoming very popular due to
sharing of the data between vehicles and the infrastructure. The sharing of data is very …

In this work, we discuss our successful efforts for industry deployment of a cryptographic
secure computation protocol. The problem we consider is privately computing aggregate …

With the rapid development of information technology, Big data has become a hot topic of
research in governments, academia, and enterprises. On the one hand, Big data brings …

Privacy issue is a main concern restricting data sharing and cross-organization
collaborations. While Privacy-Preserving Machine Learning techniques such as Multi-Party …

Existing approaches to secure multiparty computation (MPC) require all participants to
commit to the entire duration of the protocol. As interest in MPC continues to grow, it is …

The US federal court system is exploring ways to improve the accountability of electronic
surveillance, an opaque process often involving cases sealed from public view and tech …

We revisit the problem of designing scalable protocols for private statistics and private
federated learning when each device holds its private data. Locally differentially private …