Trustworthy Pre-processing of Sensor Data in Data On-Chaining Workflows for Blockchain-Based IoT Applications

Autor:	Stefan Tai, Jonathan Heiss, Anselm Busse
Rok vydání:	2021
Předmět:	Workflow Computer science business.industry Distributed computing Computation Data integrity Chaining Provisioning Cryptography Mathematical proof business Implementation
Zdroj:	Service-Oriented Computing ISBN: 9783030914301 ICSOC
Popis:	Prior to provisioning sensor data to smart contracts, a pre-processing of the data on intermediate off-chain nodes is often necessary. When doing so, originally constructed cryptographic signatures cannot be verified on-chain anymore. This exposes an opportunity for undetected manipulation and presents a problem for applications in the Internet of Things where trustworthy sensor data is required on-chain. In this paper, we propose trustworthy pre-processing as enabler for end-to-end sensor data integrity in data on-chaining workflows. We define requirements for trustworthy pre-processing, present a model and common workflow for data on-chaining, select off-chain computation utilizing Zero-knowledge Proofs (ZKPs) and Trusted Execution Environments (TEEs) as promising solution approaches, and discuss both our proof-of-concept implementations and initial experimental, comparative evaluation results. The importance of trustworthy pre-processing and principle solution approaches are presented, addressing the major problem of end-to-end sensor data integrity in blockchain-based IoT applications.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::eda6770eecfa318694dba2be3202eed3 https://doi.org/10.1007/978-3-030-91431-8_9 Zobrazit plný text záznamu