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2018 IEEE International Conference on Cloud Engineering (IC2E) (2018)
Orlando, FL, US
Apr 17, 2018 to Apr 20, 2018
ISBN: 978-1-5386-5008-0
pp: 357-363
ABSTRACT
Hyperledger Fabric is a "permissioned" blockchain architecture, providing a consistent distributed ledger, shared by a set of "peers." As with every blockchain architecture, the core principle of Hyperledger Fabric is that all the peers must have the same view of the shared ledger, making it challenging to support private data for the different peers. Extending Hyperledger Fabric to support private data (that can influence transactions) would open the door to many exciting new applications, in areas from healthcare to commerce, insurance, finance, and more. In this work we explored adding private-data support to Hyperledger Fabric using secure multiparty computation (MPC). Specifically, in our solution the peers store on the chain encryption of their private data, and use secure MPC whenever such private data is needed in a transaction. This solution is very general, allowing in principle to base transactions on any combination of public and private data. We created a demo of our solution over Hyperledger Fabric v1.0, implementing a bidding system where sellers can list assets on the ledger with a secret reserve price, and bidders publish their bids on the ledger but keep secret the bidding price itself. We implemented a smart contract (aka "chaincode") that runs the auction on this secret data, using a simple secure-MPC protocol that was built using the EMP-toolkit library. The chaincode itself was written in Go, and we used the SWIG library to make it possible to call our protocol implementation in C++. We identified two basic services that should be added to Hyperledger Fabric to support our solution, and are now working on implementing them.
INDEX TERMS
C++ language, data privacy, financial data processing, security of data, transaction processing
CITATION

F. Benhamouda, S. Halevi and T. Halevi, "Supporting Private Data on Hyperledger Fabric with Secure Multiparty Computation," 2018 IEEE International Conference on Cloud Engineering (IC2E), Orlando, FL, US, 2018, pp. 357-363.
doi:10.1109/IC2E.2018.00069
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