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 …

With the rapid advancement of artificial intelligence technology, the usage of machine
learning models is gradually becoming part of our daily lives. High-quality models rely not …

Training contemporary AI models requires investment in procuring learning data and
computing resources, making the models intellectual property of the owners. Popular model …

This paper introduces Zero-Knowledge Location Privacy (ZKLP), enabling users to prove to
third parties that they are within a specified geographical region while not disclosing their …

We consider how to design an anonymous data collection protocol that enforces compliance
rules. Imagine that each client contributes multiple data items (eg, votes, location crumbs, or …

Coding theory revolves around the incorporation of redundancy into transmitted symbols,
computation tasks, and stored data to guard against adversarial manipulation. However …

In recent years, cloud vendors have started to supply paid services for data analysis by
providing interfaces of their well-trained neural network models. However, customers lack …

Modern SNARK designs usually feature a frontend-backend paradigm: The frontend
compiles a user's program into some equivalent circuit representation, while the backend …

Due to the risks of correctness and security in outsourced cloud computing, we consider a
new paradigm called crowdsourcing: distribute tasks, receive answers and aggregate the …

Zero-knowledge (ZK) proofs have been recently explored for the integrity of machine
learning (ML) inference. However, these protocols suffer from high computational overhead …