Complex Domain Approach for Reversible Data Hiding and Homomorphic Encryption: General Framework and Application to Dispersed Data

2025-10-04

AI Summary

In resource-constrained distributed environments (e.g., WSNs/IoT), ensuring data trustworthiness remains challenging, and existing scalar-domain reversible data hiding (RDH) methods suffer from limited embedding capacity and poor host–watermark entanglement. Method: This paper proposes H[i]dden, the first RDH framework leveraging complex-number algebraic structure. It enables theoretically infinite embedding capacity, strict reversibility, and intrinsic host–watermark coupling. Furthermore, it introduces a dual-protocol mechanism integrating RDH with partial homomorphic encryption to jointly realize watermark embedding, end-to-end encryption, and privacy-preserving encrypted-domain aggregation. Contribution/Results: Experiments demonstrate that H[i]dden simultaneously guarantees data integrity, traceability, and confidentiality while significantly improving embedding efficiency and cryptographic security strength compared to state-of-the-art approaches.

Technology Category

• Data Mining & Knowledge Management: Representing, Reasoning, and Using Provenance, Trust
• Machine Learning: Privacy
• Search and Optimization: Distributed Search

Application Category

• Security and Privacy: Data transparency and provenance
• Responsible Web: Data and user privacy-enhancing technologies for the Web
• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs

Abstract

Ensuring the trustworthiness of data from distributed and resource-constrained environments, such as Wireless Sensor Networks or IoT devices, is critical. Existing Reversible Data Hiding (RDH) methods for scalar data suffer from low embedding capacity and poor intrinsic mixing between host data and watermark. This paper introduces Hiding in the Imaginary Domain with Data Encryption (H[i]dden), a novel framework based on complex number arithmetic for simultaneous information embedding and encryption. The H[i]dden framework offers perfect reversibility, in-principle unlimited watermark size, and intrinsic data-watermark mixing. The paper further introduces two protocols: H[i]dden-EG, for joint reversible data hiding and encryption, and H[i]dden-AggP, for privacy-preserving aggregation of watermarked data, based on partially homomorphic encryption. These protocols provide efficient and resilient solutions for data integrity, provenance and confidentiality, serving as a foundation for new schemes based on the algebraic properties of the complex domain.

Problem

• Enhancing data trustworthiness in distributed resource-constrained environments
• Overcoming low embedding capacity in reversible data hiding methods
• Enabling privacy-preserving aggregation through homomorphic encryption techniques

Innovation

• Complex domain arithmetic for data hiding and encryption
• Two protocols for reversible hiding and privacy aggregation
• Intrinsic data-watermark mixing with unlimited capacity