  The success of machine learning (ML) has been accompanied by increased
concerns about its trustworthiness. Several jurisdictions are preparing ML
regulatory frameworks. One such concern is ensuring that model training data
has desirable distributional properties for certain sensitive attributes. For
example, draft regulations indicate that model trainers are required to show
that training datasets have specific distributional properties, such as
reflecting diversity of the population. We propose the notion of property
attestation allowing a prover (e.g., model trainer) to demonstrate relevant
distributional properties of training data to a verifier (e.g., a customer)
without revealing the data. We present an effective hybrid property attestation
combining property inference with cryptographic mechanisms.
