  Data trading is essential to accelerate the development of data-driven
machine learning pipelines. The central problem in data trading is to estimate
the utility of a seller's dataset with respect to a given buyer's machine
learning task, also known as data valuation. Typically, data valuation requires
one or more participants to share their raw dataset with others, leading to
potential risks of intellectual property (IP) violations. In this paper, we
tackle the novel task of preemptively protecting the IP of datasets that need
to be shared during data valuation. First, we identify and formalize two kinds
of novel IP risks in visual datasets: data-item (image) IP and statistical
(dataset) IP. Then, we propose a novel algorithm to convert the raw dataset
into a sanitized version, that provides resistance to IP violations, while at
the same time allowing accurate data valuation. The key idea is to limit the
transfer of information from the raw dataset to the sanitized dataset, thereby
protecting against potential intellectual property violations. Next, we analyze
our method for the likely existence of a solution and immunity against
reconstruction attacks. Finally, we conduct extensive experiments on three
computer vision datasets demonstrating the advantages of our method in
comparison to other baselines.
