Nowadays, with the development of artificial intelligence (AI), privacy issues attract wide
attention from society and individuals. It is desirable to make the data available but invisible …

Researchers have started to recognize the necessity for a well-defined ML governance
framework based on the principle of decentralization and comprehensively defining its …

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 …

With the proliferation of machine learning, the cloud server has been employed to collect
massive data and train machine learning models. Several privacy-preserving machine …

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 …

The recent surge in artificial intelligence (AI), characterized by the prominence of large
language models (LLMs), has ushered in fundamental transformations across the globe …

Machine learning (ML) is increasingly used behind closed systems and APIs to make
important decisions. For example, social media uses ML-based recommendation algorithms …

A zero-knowledge proof of training (zkPoT) enables a party to prove that they have correctly
trained a committed model based on a committed dataset without revealing any additional …

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 …

Zero-knowledge Succinct Non-interactive ARguments of Knowledge (zkSNARKs) allow a
prover to convince a verifier of the correct execution of a large computation in private and …