Benchmark-Normalized Systematic Literature Review of Attribute-Based Encryption Overhead in Blockchain-Based Cloud Data Management

This paper set out to address a persistent obstacle in the literature on ABE for blockchain-based cloud data management: the incomparability of reported overheads across studies conducted under heterogeneous hardware, libraries, security levels, policy depths, and consensus settings. The Benchmark-Normalized SLR Framework addresses this impediment by conceptualizing the ABE module as a benchmark-conscious, overhead-categorized, and evidence-standardized subsystem. Its Benchmark Normalization Kernel maps raw stage-wise measurements from thirty primary studies into a dimensionless space, its Overhead Pattern Classifier assigns each scheme to one of four reproducible overhead classes through Ward-linkage clustering and decision-rule refinement, and its PRISMA-Guided Evidence Synthesizer links normalized costs to security assumptions, revocation methods, and deployment suitability while closing the loop between synthesized evidence and design decisions. The synthesis indicated the apparent disagreement among primary studies is largely attributable to measurement conditions rather than to cryptographic design, and the residual variation organizes into pairing-bound, policy-size-bound, revocation-heavy, and communication-efficient patterns whose trade-offs are systematic rather than incidental. Sensitivity analysis substantiated the endurance of these classes under altered correction factors and substitute linkage criteria, while simultaneously bounding the framework’s assertions: normalized values afford dependable ranking and classification yet ought not to be interpreted as exact latency forecasts. The framework’s principal value consists in transforming heterogeneous literature evidence into a constraint-aware selection instrument, thereby permitting designers of permissioned blockchain-cloud stacks to shortlist ABE schemes on comparable grounds and substantiate their choices via targeted measurements. Extending the normalization kernel to post-quantum and multi-authority constructions, and governing automated scheme selection through versioned evidence and human review, define the most immediate directions for continued work.

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