Exposing Privacy Risks in Anonymizing Clinical Data: Combinatorial Refinement Attacks on-Anonymity Without Auxiliary Information

Somiya Chhillar, Mary K. Righi, Rebecca E. Sutter, Evgenios M. Kornaropoulos

Abstract: Despite longstanding criticism from the privacy community, k-anonymity remains a widely used standard for data anonymization, mainly due to its simplicity, regulatory alignment, and preservation of data utility. However, non-experts often defend k-anonymity on the grounds that, in the absence of auxiliary information, no known attacks can compromise its protections.In this work, we refute this claim by introducing Combinatorial Refinement Attacks (CRA), a new class of privacy attacks targeting k-anonymized datasets produced using local recoding. This is the first method that does not rely on external auxiliary information or assumptions about the underlying data distribution. CRA leverages the utility-optimizing behavior of local recoding anonymization of ARX, which is a widely used open-source software for anonymizing data in clinical settings, to formulate a linear program that significantly reduces the space of plausible sensitive values. To validate our findings, we partnered with a network of free community health clinics, an environment where (1) auxiliary information is indeed hard to find due to the population they serve and (2) open-source k-anonymity solutions are attractive due to regulatory obligations and limited resources. Our results on real-world clinical microdata reveal that even in the absence of external information, established anonymization frameworks do not deliver the promised level of privacy, raising critical privacy concerns.

External IDs:dblp:conf/ccs/ChhillarRSK25