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Decentralized Privacy-preserving Timed Execution in Blockchain-based Smart Contract Platforms

Li, Chao and Palanisamy, Balaji (2018) Decentralized Privacy-preserving Timed Execution in Blockchain-based Smart Contract Platforms. In: 25th IEEE International Conference on High Performance Computing, Data, and Analytics, 12-19 Dec 2018, Marathahalli, Bengaluru, India.

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Timed transaction execution is critical for various decentralized privacy-preserving applications powered by blockchain-based smart contract platforms. Such privacy-preserving smart contract applications need to be able to securely maintain users' sensitive inputs off the blockchain until a prescribed execution time and then automatically make the inputs available to enable on-chain execution of the target function at the execution time, even if the user goes offline. While straight-forward centralized approaches provide a basic solution to the problem, unfortunately they are limited to a single point of trust. This paper presents a new decentralized privacy-preserving transaction scheduling approach that allows users of Ethereum-based decentralized applications to schedule transactions without revealing sensitive inputs before an execution time window selected by the users. The proposed approach involves no centralized party and allows users to go offline at their discretion after scheduling a transaction. The sensitive inputs are privately maintained by a set of trustees randomly selected from the network enabling the inputs to be revealed only at the execution time. The proposed protocol employs secret key sharing and layered encryption techniques and economic deterrence models to securely protect the sensitive information against possible attacks including some trustees destroying the sensitive information or secretly releasing the sensitive information prior to the execution time. We demonstrate the attack-resilience of the proposed approach through rigorous analysis. Our implementation and experimental evaluation on the Ethereum official test network demonstrates that the proposed approach is effective and has a low gas cost and time overhead associated with it.


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Item Type: Conference or Workshop Item (Paper)
Status: Published
CreatorsEmailPitt UsernameORCID
Li, Chaochl205@pitt.educhl205
Date: 2018
Event Title: 25th IEEE International Conference on High Performance Computing, Data, and Analytics
Event Dates: 12-19 Dec 2018
Event Type: Conference
Schools and Programs: School of Computing and Information > Information Science
Refereed: Yes
Date Deposited: 25 Feb 2019 15:48
Last Modified: 25 Feb 2019 15:48

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