Zero-knowledge proof is a powerful cryptographic primitive that has found various
applications in the real world. However, existing schemes with succinct proof size suffer from …

Deep learning techniques with neural networks are developing prominently in recent years
and have been deployed in numerous applications. Despite their great success, in many …

How can a model owner prove they trained their model according to the correct
specification? More importantly, how can they do so while preserving the privacy of the …

Secure multi-party computation (MPC) allows a set of parties to jointly compute a function on
their private inputs, and reveals nothing but the output of the function. In the last decade …

Federated machine learning (FL) allows to collectively train models on sensitive data as only
the clients' models and not their training data need to be shared. However, despite the …

Outsourced computation for neural networks allows users access to state-of-the-art models
without investing in specialized hardware and know-how. The problem is that the users lose …

A zero-knowledge (ZK) proof guarantees that the result of a computation is correct while
keeping part of the computation details private. Some ZK proofs are tiny and can be verified …

Reproducibility is crucial to the advancement of science; it strengthens confidence in
seemingly contradictory results and expands the boundaries of known discoveries …

A decade of active research has led to practical constructions of zero-knowledge succinct
non-interactive arguments of knowledge (zk-SNARKs) that are now being used in a wide …

This paper initiates research on zero-knowledge middleboxes (ZKMBs). A ZKMB is a
network middlebox that enforces network usage policies on encrypted traffic. Clients send …