Paper Info
Title
Amanuensis: provenance, privacy, and permission in TEE-enabled blockchain data systems
Abstract
Blockchain technology is heralded for its ability to provide transparent and immutable audit trails for data shared among semi-trusted parties. With the addition of smart contracts, blockchains can track and verify arbitrary computations – which enables blockchain users to verify the provenance of information derived from data through the blockchain. This provenance comes at the cost of data confidentiality and user privacy, however, which is unacceptable for many sensitive applications. The need for verifiable yet confidential data sharing and computation has led some to add trusted execution environment (TEE) hardware to blockchain platforms. By moving sensitive operations (e.g., data decryption and analysis) off of the blockchain and into a TEE, they get both the confidentiality of TEEs and the transparency of blockchains without the need to completely trust any one party in the data-sharing ecosystem.In this paper, we build on our TEE-enabled blockchain data-sharing system, Amanuensis, to ensure the freshness of access-control lists shared between the blockchain and TEE, and to improve the privacy of users interacting within the system. We also detail how TEE-based remote attestation help us to achieve information provenance – specifically, how to achieve information provenance in the context of the Intel SGX trusted execution environment. Finally, we present an evaluation of our system, in which we test several real-world machine-learning applications (logistic regression, kNN, SVM) to determine the run-time overhead of information confidentiality and provenance. Each machine-learning program exhibited a slowdown between 1.1 and 2.8x when run inside of our confidential environment, and took an average of 59 milliseconds to verify the provenance of an input data set.
Year
DOI
Venue
2022
10.1109/ICDCS54860.2022.00023
2022 IEEE 42nd International Conference on Distributed Computing Systems (ICDCS)
Keywords
DocType
ISSN
TEE-enabled blockchain data systems,blockchain technology,transparent audit trails,immutable audit trails,semitrusted parties,blockchain users,data confidentiality,user privacy,confidential data,trusted execution environment hardware,blockchain platforms,data decryption,data-sharing ecosystem,TEE-enabled blockchain data-sharing system,TEE-based remote attestation help,information provenance,information confidentiality,real-world machine-learning applications,logistic regression,kNN,SVM
Conference
1063-6927
ISBN
Citations 
PageRank 
978-1-6654-7178-7
0
0.34
References 
Authors
20
2
Name
Order
Citations
PageRank
Taylor Hardin100.34
David Kotz24292430.36