The researchers’ mathematical framework also applies to the problem of data privacy, or how much information can be gleaned from aggregated — and supposedly “anonymized” — data about Internet users’ online histories. If, for instance, Netflix releases data about users’ movie preferences, is it also inadvertently releasing data about their political preferences? Calmon and his colleagues’ technique could help data managers either modify aggregated data or structure its presentation in a way that minimizes the risk of privacy compromises.