Privacy-Preserving Stealthy FDIA Detection in Smart Grids: Integrating Differential Privacy, Secure Multi-Party Computation, and Permissioned Blockchains

• Adaan Ahsun

  Covenant University

  Author

The transition of conventional power grids into smart grid infrastructures has introduced sophisticated cybersecurity vulnerabilities, particularly stealthy false data injection attacks (FDIAs) that evade traditional bad data detection mechanisms while compromising state estimation and grid stability. Existing detection frameworks predominantly rely on centralized data aggregation, creating critical privacy concerns that deter utility participation and expose sensitive consumer consumption patterns. This study addresses the gap in privacy-preserving FDIA detection by proposing an integrated framework combining differential privacy for measurement perturbation, secure multi-party computation for encrypted anomaly scoring, and permissioned blockchain for tamper-proof audit trails. The methodology employs a design-based research approach validated through simulations on IEEE 14-bus and 57-bus test systems, implementing local differential privacy with Laplacian noise injection and garbled circuit-based secure computation protocols. Experimental results demonstrate that the proposed framework achieves 89.4% detection accuracy for stealthy FDIAs with a false positive rate of 3.2%, while preserving differential privacy guarantees at ε=0.5 and maintaining computational latency below 250 milliseconds per detection cycle. The framework extends Mehedi et al. (2026) by demonstrating that cryptographic privacy preservation need not sacrifice detection efficacy, offering a replicable architecture for utility deployment that balances regulatory compliance with operational security requirements.

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Copyright (c) 2026 Adaan Ahsun (Author)

This work is licensed under a Creative Commons Attribution 4.0 International License.