Unexpected Manipulation in Blockchain E-Voting: Detecting and Theorizing “Invisible” Strategic Failures

While blockchain e-voting systems (see Gochhi et al. 2024; Saranya G et al. 2025) tout immutability and transparency, most security proofs implicitly assume rational, static voter behavior and overlook emergent, “invisible” strategies—like timing attacks, vote delegation collusion, or subtle smart contract exploits. This project proposes a hybrid empirical-theoretical approach: deploy blockchain e-voting prototypes in controlled simulations, use anomaly detection (inspired by bug detection approaches like Harzevili et al. 2024), and systematically catalog deviations from expected honest or standard manipulative behavior. The novelty is in linking observed anomalies to new theoretical models of “unexpected” manipulation—for instance, strategy-proofness failures that arise not from preference misreporting, but from exploiting blockchain-specific features (e.g., gas fees, transaction ordering). This could reveal whole new classes of vulnerabilities and guide the design of more robustly strategy-proof blockchain voting protocols.

References:

1. Decentralized E-Voting System: Enhancing Security and Trust using Ethereum Blockchain. Saranya G, Kumaran K, Rishvan Kumar. R (2025). 2025 Third International Conference on Augmented Intelligence and Sustainable Systems (ICAISS).
2. Checker Bug Detection and Repair in Deep Learning Libraries. Nima Shiri Harzevili, Mohammad Mahdi Mohajer, Jiho Shin, Moshi Wei, Gias Uddin, Jinqiu Yang, Junjie Wang, Song Wang, Zhen Ming Jiang, Nachiappan Nagappan (2024). arXiv.org.
3. Blockchain-Based Comparative Analysis of E-Voting Systems: A Review. Soubhagya Kumar Gochhi, Soumya Sahoo, Prabeer Kishore Samanta, Sanjeeb Kumar Panda, Jyoti Ranjan Sahoo (2024). 2024 International Conference on Advancements in Smart, Secure and Intelligent Computing (ASSIC).