There is a significant conceptual gap between legal and mathematical thinking around data privacy. The effect is uncertainty as to the which technical offerings adequately match expectations expressed in legal standards. The uncertainty is exacerbated by a litany of successful privacy attacks, demonstrating that traditional statistical disclosure limitation techniques often fall short of the sort of privacy envisioned by legal standards.

We define predicate singling out, a new type of privacy attack intended to capture the concept of singling out appearing in the General Data Protection Regulation (GDPR).

Informally, an adversary predicate singles out a dataset X using the output of a data release mechanism M(X) if it manages to a predicate p matching exactly one row in X with probability much better than a statistical baseline. A data release mechanism that precludes such attacks is secure against predicate singling out (PSO secure).

We argue that PSO security is a mathematical concept with legal consequences. Any data release mechanism that purports to "render anonymous'' personal data under the GDPR must be secure against singling out, and hence must be PSO secure. We then analyze PSO security, showing that it fails to self-compose. Namely, a combination of $\omega(\log n)$ exact counts, each individually PSO secure, enables an attacker to predicate single out. In fact, the composition of just two PSO secure mechanisms can fail to provide PSO security.

Finally, we ask whether differential privacy and k-anonymity are PSO secure. Leveraging a connection to statistical generalization, we show that differential privacy implies PSO security. However, k-anonymity does not: there exists a simple and general predicate singling out attack under mild assumptions on the k-anonymizer and the data distribution.