Functional adaptor signatures (FAS) are a novel cryptographic primitive introduced at CCS’24 that enable privacy-preserving, fine-grained data-payment exchanges between a seller and a buyer in a trustless and atomic manner. In this setup, the seller holds sensitive data x (e.g., patient records, climate data), and the buyer specifies a function f (e.g., aggregate, sum). FAS guarantees that the buyer learns f (x) (and nothing beyond) if and only if the seller receives payment in blockchain-based tokens. Unlike generic smart contracts, FAS-powered solutions excel in privacy, efficiency, and compatibility with diverse blockchain systems. However, prior FAS constructions were limited to linear functions (where f was linear in x), restricting their applicability to more complex and prevalent applications including data analytics and ML model evaluations.
In this work, we extend the capabilities of FAS to support higher-degree functions (deg ≥ 2), significantly broadening its range of applications. Our core contribution is a novel FAS design leveraging homomorphic encryption, which simultaneously achieves enhanced efficiency and compatibility for general functions. This approach diverges fundamentally from the restricted design in CCS’24 which relied on connections to functional encryption. We implement our homomorphic encryption-based FAS for functions arising in applications such as data analytics and machine learning inference. Remarkably, even for linear functions, our new design achieves an order-of-magnitude improvement in performance compared to CCS’24 constructions. Furthermore, our solutions seamlessly integrate with prominent blockchain systems, requiring only a basic signature verification script on standard transactions, thus ensuring practical deployability. As a conceptual contribution, we introduce the general paradigm of a blockchain-based functional fair exchange (FFE) protocol, rigorously define buyer and seller fairness, and show that FAS implies the general goal of FFE.