  We study valuing the data of a data owner/seller for a data seeker/buyer.
Data valuation is often carried out for a specific task assuming a particular
utility metric, such as test accuracy on a validation set, that may not exist
in practice. In this work, we focus on task-agnostic data valuation without any
validation requirements. The data buyer has access to a limited amount of data
(which could be publicly available) and seeks more data samples from a data
seller. We formulate the problem as estimating the differences in the
statistical properties of the data at the seller with respect to the baseline
data available at the buyer. We capture these statistical differences through
second moment by measuring diversity and relevance of the seller's data for the
buyer; we estimate these measures through queries to the seller without
requesting raw data. We design the queries with the proposed approach so that
the seller is blind to the buyer's raw data and has no knowledge to fabricate
responses to queries to obtain a desired outcome of the diversity and relevance
trade-off.We will show through extensive experiments on real tabular and image
datasets that the proposed estimates capture the diversity and relevance of the
seller's data for the buyer.
