Karl Wüst (ETH Zurich), Loris Diana (ETH Zurich), Kari Kostiainen (ETH Zurich), Ghassan Karame (NEC Labs), Sinisa Matetic (ETH Zurich), Srdjan Capkun (ETH Zurich)

In this paper we propose Bitcontracts, a novel solution that enables secure and efficient execution of generic smart contracts on top of unmodified legacy cryptocurrencies like Bitcoin that do not support contracts natively. The starting point of our solution is an off-chain execution model, where the contract's issuers appoints a set of service providers to execute the contract's code. The contract's execution results are accepted if a quorum of service providers reports the same result and clients are free to choose which such contracts they trust and use. The main technical contribution of this paper is how to realize such a trust model securely and efficiently without modifying the underlying blockchain.

We also identify a set of generic properties that a blockchain system must support so that expressive smart contracts can be added safely, and analyze popular existing blockchains based on these criteria.

View More Papers

KUBO: Precise and Scalable Detection of User-triggerable Undefined Behavior...

Changming Liu (Northeastern University), Yaohui Chen (Facebook Inc.), Long Lu (Northeastern University)

Read More

Detecting Tor Bridge from Sampled Traffic in Backbone Networks

Hua Wu (School of Cyber Science & Engineering and Key Laboratory of Computer Network and Information Integration Southeast University, Ministry of Education, Jiangsu Nanjing, Purple Mountain Laboratories for Network and Communication Security (Nanjing, Jiangsu)), Shuyi Guo, Guang Cheng, Xiaoyan Hu (School of Cyber Science & Engineering and Key Laboratory of Computer Network and Information Integration…

Read More

Demo #7: Automated Tracking System For LiDAR Spoofing Attacks...

Yulong Cao, Jiaxiang Ma, Kevin Fu (University of Michigan), Sara Rampazzi (University of Florida), and Z. Morley Mao (University of Michigan) Best Demo Award Runner-up ($200 cash prize)!

Read More

Reinforcement Learning-based Hierarchical Seed Scheduling for Greybox Fuzzing

Jinghan Wang (University of California, Riverside), Chengyu Song (University of California, Riverside), Heng Yin (University of California, Riverside)

Read More