A Survey on Secure and Scalable Cross-chain Provenance Techniques For Digital Forensics

Abstract

Investigations of cybercrime today require forensic architectures that natively traverse multiple blockchains with ease while protecting and scaling evidence processing. Although blockchains support tamper- evident logs, their original single-chain architecture limits cross-platform interoperability and forensic scaling. Recent developments overcome these limitations such as zero-knowledge proofs supporting private but verifiable evidence verification, sharding architectures splitting state without compromising latency, and AI-based anomaly detectors identifying subtle tampering. But challenges remains like zero- knowledge proofs are computationally expensive, sharding poses intricate state-consistency problems and AI models need to be retrained constantly, incurring operational burden. Future research needs to make these pieces work for real- time, large-scale forensic applications by designing light-weight zero-knowledge constructs, self-tuning shard governance systems and compact AI with incremental-update threads. Integrating such abilities into single frameworks will offer privacy, scalability and security, supporting forensic processes for which courts will give credit in various, changing block-chain environments.