  The proliferation of data collection and machine learning techniques has
created an opportunity for commercialization of private data by data
aggregators. In this paper, we study this data monetization problem using a
contract-theoretic approach. Our proposed adversarial contract design framework
accounts for the heterogeneity in honest buyers' demands for data, as well as
the presence of adversarial buyers who may purchase data to compromise its
privacy. We propose the notion of Price of Adversary (PoAdv) to quantify the
effects of adversarial users on the data seller's revenue, and provide bounds
on the PoAdv for various classes of adversary utility. We also provide a fast
approximate technique to compute contracts in the presence of adversaries.
