@inproceedings{4a7f6e9da69f45c6938db036cb5deedc,
title = "Fast consistency auditing for massive industrial data in untrusted cloud services",
abstract = "Cloud service is an essential technique to provide product traceability for industrial systems by providing data integration and sharing services. However, a malicious or corrupted cloud service may prevent industrial participants from acquiring accurate and consistent traceability of products. To fix this issue, we propose Acics, a fast consistency auditing scheme for massive industrial data in untrusted cloud services. Our scheme enables industrial participants to circularly play the role of the auditor to audit data consistency of products in real-time. Additionally, we design a separated storage mechanism to improve the auditing efficiency for massive industrial data by utilizing and tailoring ORAM. The evaluation indicates that our solution audits data consistency with reasonable cost.",
keywords = "Cloud services, Data consistency, Industrial system, ORAM",
author = "Jingxian Cheng and Saiyu Qi and Wenqing Wang and Yuchen Yang and Yong Qi",
note = "Publisher Copyright: {\textcopyright} 2020 Association for Computing Machinery.; 30th Great Lakes Symposium on VLSI, GLSVLSI 2020 ; Conference date: 07-09-2020 Through 09-09-2020",
year = "2020",
month = sep,
day = "7",
doi = "10.1145/3386263.3407598",
language = "英语",
series = "Proceedings of the ACM Great Lakes Symposium on VLSI, GLSVLSI",
publisher = "Association for Computing Machinery",
pages = "381--386",
booktitle = "GLSVLSI 2020 - Proceedings of the 2020 Great Lakes Symposium on VLSI",
}