Publication Type

Journal Article

Version

publishedVersion

Publication Date

3-2026

Abstract

Privacy-preserving information queries enable a requester to obtain only the value f(x) computed over sensitive data x, while preventing disclosure of the underlying records. Existing approaches typically reveal full data, incur high on-chain overhead, or lack fair and verifiable delivery of function outputs. We propose a general-purpose, blockchain-compatible framework that ensures the requester learns only f(x) with no extra leakage and that the provider receives fair payment. The design integrates Adaptor Signatures (AS) for fair exchange and Inner-Product Functional Encryption (IPFE) for fine-grained function extraction. The framework is domain-agnostic and applicable to privacy-sensitive applications such as medical insurance and financial risk assessment. We formally prove advertisement soundness, unforgeability, witness extractability, and witness privacy. A prototype demonstrates linear scalability up to ℓ=100,000. We conduct a comparative evaluation between our scheme and related works under typical attribute dimensions . The results show that, under the same dimensional settings, our scheme achieves a 2.5×-6× performance improvement. Meanwhile, the storage overhead of our solution ranges from 1.6 KB to 4.1 KB, which is slightly lower than that of existing schemes (2.7-4.0 KB), and the on-chain cost consistently remains a fixed 64 bytes. These findings demonstrate that our approach provides significant practical advantages in low-dimensional, high-frequency medical query scenarios.

Keywords

Private information query, Fair exchange, Functional encryption, Adaptor signatures, Blockchain

Discipline

Information Security

Publication

Journal of Information Security and Applications

Volume

97

First Page

1

Last Page

12

ISSN

2214-2126

Identifier

10.1016/j.jisa.2025.104359

Publisher

Elsevier

Additional URL

https://doi.org/10.1016/j.jisa.2025.104359

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