TY - GEN
T1 - A Blockchain-Based Dynamic Game Incentive Mechanism for Privacy-Preserving Computation
AU - Pan, Sicheng
AU - Tang, Jiayi
AU - Li, Youqi
AU - Zuo, Cong
AU - Wang, Licheng
AU - Yuan, Yanli
AU - Zhu, Liehuang
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.
PY - 2026
Y1 - 2026
N2 - Privacy-preserving computation has become a critical component in data collaboration, yet existing systems often suffer from high cryptographic overhead and lack effective incentives for honest participation. To address these issues, we propose a blockchain-based dynamic game incentive mechanism for privacy-preserving computation, which uses a combination of rewards and regulatory penalties to incentivize rational participants, who aim to maximize their interests, to remain honest in secure multi-party computation (MPC). Building on this approach, we introduce a Shapley value-based reward distribution mechanism to ensure fair compensation according to each participant’s actual contribution. To maintain low on-chain cost while preventing manipulation, we introduce a seed-driven deterministic consensus procedure that enables off-chain Monte Carlo approximation of Shapley values while ensuring on-chain verifiability. The entire incentive and verification process is automated through smart contracts, ensuring transparency, verifiability, and trustless execution. Analysis shows that under rational assumptions, honest computation becomes the dominant strategy, prompting participants to proactively engage in honest computation to maximize their own gains. Our results provide a practical and incentive-compatible framework for fair, efficient, and trustworthy privacy-preserving computation in decentralized environments.
AB - Privacy-preserving computation has become a critical component in data collaboration, yet existing systems often suffer from high cryptographic overhead and lack effective incentives for honest participation. To address these issues, we propose a blockchain-based dynamic game incentive mechanism for privacy-preserving computation, which uses a combination of rewards and regulatory penalties to incentivize rational participants, who aim to maximize their interests, to remain honest in secure multi-party computation (MPC). Building on this approach, we introduce a Shapley value-based reward distribution mechanism to ensure fair compensation according to each participant’s actual contribution. To maintain low on-chain cost while preventing manipulation, we introduce a seed-driven deterministic consensus procedure that enables off-chain Monte Carlo approximation of Shapley values while ensuring on-chain verifiability. The entire incentive and verification process is automated through smart contracts, ensuring transparency, verifiability, and trustless execution. Analysis shows that under rational assumptions, honest computation becomes the dominant strategy, prompting participants to proactively engage in honest computation to maximize their own gains. Our results provide a practical and incentive-compatible framework for fair, efficient, and trustworthy privacy-preserving computation in decentralized environments.
KW - Blockchain
KW - Dynamic game theory
KW - Incentive mechanism
KW - Privacy-preserving computation
UR - https://www.scopus.com/pages/publications/105039860588
U2 - 10.1007/978-3-032-21177-4_10
DO - 10.1007/978-3-032-21177-4_10
M3 - Conference contribution
AN - SCOPUS:105039860588
SN - 9783032211767
T3 - Communications in Computer and Information Science
SP - 177
EP - 195
BT - Emerging Information Security and Applications - 6th International conference, EISA 2025, Proceedings
A2 - Li, Wenjuan
A2 - Katsikas, Sokratis
A2 - Shao, Jun
PB - Springer Science and Business Media Deutschland GmbH
T2 - 6th International Conference on Emerging Information Security and Applications, EISA 2025
Y2 - 12 December 2025 through 13 December 2025
ER -