Title

Amanuensis: provenance, privacy, and permission in TEE-enabled blockchain data systems

Author ORCID Identifier

Taylor Hardin: https://orcid.org/0000-0002-9613-9765

David Kotz: https://orcid.org/0000-0001-7411-2783

Document Type

Conference Paper

Publication Date

7-2022

Publication Title

Proceedings of the IEEE International Conference on Distributed Computing Systems

Department

Department of Computer Science

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.

DOI

10.1109/ICDCS54860.2022.00023

Original Citation

Taylor Hardin and David Kotz. Amanuensis: provenance, privacy, and permission in TEE-enabled blockchain data systems. Proceedings of the IEEE International Conference on Distributed Computing Systems, pages 144–156. IEEE, July 2022. doi:10.1109/ICDCS54860.2022.00023.

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