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Modern data aggregation often takes the form of a platform collecting datafrom a network of users. More than ever, these users are now requesting thatthe data they provide is protected with a guarantee of privacy. This has led tothe study of optimal data acquisition frameworks, where the optimalitycriterion is typically the maximization of utility for the agent trying toacquire the data. This involves determining how to allocate payments to usersfor the purchase of their data at various privacy levels. The main goal of thispaper is to characterize a fair amount to pay users for their data at a givenprivacy level. We propose an axiomatic definition of fairness, analogous to thecelebrated Shapley value. Two concepts for fairness are introduced. The firsttreats the platform and users as members of a common coalition and provides acomplete description of how to divide the utility among the platform and users.In the second concept, fairness is defined only among users, leading to apotential fairness-constrained mechanism design problem for the platform. Weconsider explicit examples involving private heterogeneous data and show howthese notions of fairness can be applied. To the best of our knowledge, theseare the first fairness concepts for data that explicitly consider privacyconstraints.

The Fair Value of Data Under Heterogeneous Privacy Constraints