  The quality of learning generally improves with the scale and diversity of
data. Companies and institutions can therefore benefit from building models
over shared data. Many cloud and blockchain platforms, as well as government
initiatives, are interested in providing this type of service.
  These cooperative efforts face a challenge, which we call ``exclusivity
attacks''. A firm can share distorted data, so that it learns the best model
fit, but is also able to mislead others. We study protocols for long-term
interactions and their vulnerability to these attacks, in particular for
regression and clustering tasks. We conclude that the choice of protocol, as
well as the number of Sybil identities an attacker may control, is material to
vulnerability.
