DP17865 Digital Hermits
When a user shares multi-dimensional data about themselves with a firm, the firm learns about the correlations of different dimensions of user data. We incorporate this type of learning into a model of a data market in which a firm acquires data from users with privacy concerns. User data is multi-dimensional, and each user can share no data, only non-sensitive data, or their full data with the firm. As the firm collects more data and becomes better at drawing inferences about a user’s privacy-sensitive data from their non-sensitive data, the share of new users who share no data (“digital hermits”) grows. At the same time, the share of new users who share their full data also grows. The model therefore predicts a polarization of users’ data sharing choices away from non-sensitive data sharing to no sharing and full sharing.
