  Data contamination has become prevalent and challenging with the rise of
models pretrained on large automatically-crawled corpora. For closed models,
the training data becomes a trade secret, and even for open models, it is not
trivial to detect contamination. Strategies such as leaderboards with hidden
answers, or using test data which is guaranteed to be unseen, are expensive and
become fragile with time. Assuming that all relevant actors value clean test
data and will cooperate to mitigate data contamination, what can be done? We
propose three strategies that can make a difference: (1) Test data made public
should be encrypted with a public key and licensed to disallow derivative
distribution; (2) demand training exclusion controls from closed API holders,
and protect your test data by refusing to evaluate without them; (3) avoid data
which appears with its solution on the internet, and release the web-page
context of internet-derived data along with the data. These strategies are
practical and can be effective in preventing data contamination.
