$π$Creds: Privately Inferred Credentials
This work addresses the problem of limited deployment of decentralized verifiable credentials by enabling certification of semantically rich claims over unstructured data, but the approach is incremental as it builds on existing LLM and credential technologies.
The paper introduces Privately Inferred Credentials (πCreds), a system that uses trusted LLM inference over authenticated data to generate privacy-preserving, decentralized verifiable credentials, expanding the range of certifiable claims beyond existing systems. The authors formalize new application-level threats (SCAE and ACPP) and demonstrate a prototype over financial, health, email, and code data.
Decentralized verifiable credential systems have seen limited deployment in practice. Existing constructions, built on zero-knowledge proofs, are complex, application-specific, and largely restricted to predicates over structured data. We present Privately Inferred Credentials ($π$Creds): privacy-preserving, legacy-compatible, decentralized verifiable credentials generated by trusted LLM inference over authenticated data. LLMs' ability to semantically reason over unstructured data substantially expands the range of claims $π$Creds can certify over existing credential systems. The use of LLMs also introduces new application-level threats, which we formalize through two problems: the Source-Constrained Adversarial Example (SCAE) problem, which captures robustness against adversaries that manipulate authenticated data to obtain misleading credentials, and the Authenticated Covert Predicate Poisoning (ACPP) problem, which captures privacy leakage through adversarial model selection. We characterize applications of $π$Creds over user data, and a novel class of credentials over proprietary software that certifies properties of a service without revealing its source code. Our prototype supports issuing credentials over live financial, health, email, and code sources, and we empirically study the SCAE and ACPP threats on a product expertise credential over real financial data.