< Agenda

One Architecture, Any Silo: Proving Patterns Without Naming Victims

16:10 (30 minutes) · Room 1A · Talk · Introductory · Security

Domestic abuse reporting has a data architecture problem. To corroborate a pattern of abuse, systems currently assume the underlying raw data of the victim must be seen by whoever's verifying it. That single assumption forces an impossible trade-off: survivors either stay silent contributing to the dark figure of crime or expose sensitive data to centralised systems that become high-risk targets in their own right. This talk walks through a working Zero-Knowledge Proof architecture built to remove that trade-off entirely one where a statutory authority or third-sector agency can learn that a pattern exists. This can be a perpetrator that has been flagged by multiple independent reports, a threshold crossed, or a count without ever seeing a single name, incident report, or plaintext record. The system works by having each party for example, a police force and a support agency independently commit their records to a cryptographic hash rather than sharing the records themselves. Before that commitment happens, names go through a fuzzy-matching step so that naming variants, like "O'Neill" versus "Oneill," get grouped into the same bucket without anyone comparing raw strings directly. Each party then generates a proof from their commitment, and a verifier who never sees either party's plaintext data checks those proofs and groups them by their public phonetic identifier. When proofs from multiple independent agencies land in the same group, the verifier can say a pattern exists and how many times it's been corroborated, without ever learning a name, date of birth, or case reference. The only thing that crosses back to whoever's watching for patterns is a count and a yes/no flag nothing that could re-identify anyone. Key Takeaways: How Zero-Knowledge Proofs differ practically from Private Set Intersection and Data Clean Rooms, and why PSI's binary yes/no output can't support pattern-counting use cases A working architecture for cross-silo pattern detection where a verifying authority learns only an aggregate signal (a count crossing a threshold) never plaintext records, names, or case files The real engineering challenge of encoding fuzzy, approximate matching (handling naming variants) ahead of a zero-knowledge circuit, and what's involved in eventually moving that logic inside an arithmetic circuit A transferable architectural pattern: any scenario with multiple independent parties needing to corroborate a pattern, without a central party being trusted with raw data