design authenticated data structures

This work addresses the challenge of formally guaranteeing security and correctness in the automated generation of authenticated data structures (ADS). We introduce the first relational separation logic supporting collision-resistant hash functions and construct a bilingual semantic model in Coq to rigorously characterize type abstraction security. Based on this logic, we implement fully automatic code generation for an ADS library and mechanize proofs of its functional correctness, memory safety, and equivalence of key optimizations. Moreover, we establish the first formal framework proving secure interoperability between automatically generated code and hand-optimized implementations. Our approach integrates Iris concurrent separation logic with cryptographic hash modeling, enabling end-to-end trustworthiness under a unified formal foundation. The result is a verifiable construction paradigm for high-assurance ADS systems.

Protocol designers often face a high barrier to entry in using formal verification tools such as ProVerif and Tamarin due to the lack of systematic guidance on translating security properties into executable models. This work addresses this gap by conducting a systematic review of 53 studies published between 2022 and 2025, resulting in the first comprehensive taxonomy of security properties tailored to mainstream verification tools. The taxonomy integrates informal explanations, first-order logic definitions, and tool-specific modeling exemplars. By bridging the gap between theoretical formulations and practical modeling, this study significantly enhances the accuracy and efficiency of protocol modeling. An accompanying open-source repository of illustrative examples further lowers the practical barrier to adopting formal verification in real-world protocol design.

Existing program verification tools struggle to scale to large codebases due to their heavy reliance on manual intervention. This paper introduces Diodon, a modular verification methodology that partitions systems into a security-critical core and peripheral applications. It combines semi-automated (auto-active) verification—using Gobra to formally verify core protocol properties such as key confidentiality and injection resistance—with fully automated static analysis for the periphery. A key innovation is I/O independence verification: a static analysis technique that automatically enforces interface constraints and guarantees isolation of I/O behavior, ensuring peripheral code cannot compromise core security. Evaluated on an industrial-grade Go codebase exceeding 100,000 lines, Diodon required verification of only ~1% of the codebase as the core, achieving end-to-end security certification within three months. This approach significantly improves the feasibility and efficiency of verifying large-scale systems.

This work addresses the limitations of traditional binary trust models in trusted computing by proposing a formal trust framework grounded in category theory and Heyting algebras. Trust elements, assertions, outcomes, and decisions are modeled as objects, while proofs, verification, and decision processes are treated as morphisms. The framework introduces exponential objects to capture the compositional nature of proof operations. Notably, it is the first to integrate category theory with Heyting algebras, enabling fine-grained representation of trust levels and providing a novel metric to quantify the expressive power of proof environments. Experimental evaluations demonstrate the model’s expressiveness and scalability in scenarios including boot-run-shutdown sequences, Evil Maid attack analysis, and multi-component dynamic systems.

This work addresses the frequent disconnect between the mathematical certainty of numerical values in cryptographic protocols and their concrete representations, which undermines interoperability and formal verification. Drawing from representation theory, the paper introduces three classes of representations—algorithmically approximable, finitely precisely describable, and canonically normalizable—and proves that no universal computable canonicalizer can transform arbitrary approximate programs into a unique finite encoding. It extends the canonical encoding paradigm of the rational number system Σ_Q to practical cryptographic objects. By integrating computability theory with canonical serialization techniques, the approach is applied to symmetric and asymmetric encryption, hashing, and blockchain integrity protocols. Case studies such as Snaproot demonstrate that canonical representations are essential for achieving precise protocol specifications, ensuring interoperability, and enabling byte-level correctness arguments.

This work addresses a critical limitation in existing certification schemes for encrypted machine learning models, which only verify model behavior on a fixed audit dataset and thus fail to guarantee generalization to new, identically distributed data—rendering them vulnerable to adversarial manipulation. We formally introduce, for the first time, a generalizable security definition tailored to encrypted model certification and expose fundamental assumptions underlying current zero-knowledge proof–based privacy-preserving auditing protocols that do not hold in practical deployments. To bridge this gap, we propose a unified certification framework integrating secure multi-party computation, zero-knowledge proofs, and statistical generalization theory, providing formal guarantees that audit outcomes generalize to real-world data. Empirical evaluation demonstrates that adversaries can achieve over 99% accuracy during audits while degrading true model performance to below 30%; our protocol effectively mitigates such attacks, aligning theoretical assurances with real-world robustness.

This work addresses the high barrier to entry in formal verification of cryptographic protocols and the difficulty of tracing verification results back to concrete implementations. The authors propose a domain-specific language (DSL)-centric development methodology that pioneers a “language-first” modeling paradigm. Their approach automatically translates protocol implementations into Tamarin-verifiable models and integrates symbolic execution to ensure memory safety. This framework guarantees that general trace properties established through formal verification are correctly mapped back to the original source code. Empirical evaluation demonstrates the successful generation of precise models for Signed Diffie-Hellman and WireGuard protocols; notably, the resulting WireGuard implementation achieves interoperability, practical usability, and compositional security guarantees.

This work addresses the lack of formally verifiable, fine-grained access control mechanisms in local-first systems operating at scale under low-trust collaboration settings. We propose a bottom-up approach that integrates a capability-based authorization model with Hashed Chronicle—a replicated data type—to design a Byzantine fault-tolerant collaborative group management mechanism. For the first time, system-level formal verification is introduced into local-first access control by leveraging the Verus framework to specify and verify a Rust implementation with zero runtime overhead. We formalize the semantics and key invariants of a simplified CRDT and prove the correctness of the core authorization logic, thereby providing Matrix, Keyhive, and similar systems with an integrable, high-assurance security foundation.