IntentKeeper: Intent-oriented Data Usage Control for Federated Data Analytics

Flavio Cirillo, Bin Cheng, Raffaele Porcellana, Marco Russo, Gürkan Solmaz, Hisashi Sakamoto, S. Romano
{"title":"IntentKeeper: Intent-oriented Data Usage Control for Federated Data Analytics","authors":"Flavio Cirillo, Bin Cheng, Raffaele Porcellana, Marco Russo, Gürkan Solmaz, Hisashi Sakamoto, S. Romano","doi":"10.1109/LCN48667.2020.9314823","DOIUrl":null,"url":null,"abstract":"Data usage control is of utmost importance for federated data analytics across multiple business domains. However, the existing data usage control approaches are limited due to their complexity and inefficiency. This paper proposes an intent-oriented data usage control system for federated data analytics, called IntentKeeper. The system allows users to specify intents for data usage policies and services easily. Thus, it reduces the data sharing complexity for data providers and consumers. Moreover, IntentKeeper enforces preventive and proactive data usage control for better security and efficiency through joint decisions based on policy enforcement and service orchestration. The use case validations for the automotive industry scenario show that IntentKeeper significantly reduces the complexity of policy specification (up to 75% for moderately complex scenarios) compared to the state-of-the-art flow-based approach. Lastly, the experimental results show that the IntentKeeper system provides sufficiently short response times (less than 40ms) with minimal overhead (less than 10ms).","PeriodicalId":245782,"journal":{"name":"2020 IEEE 45th Conference on Local Computer Networks (LCN)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 IEEE 45th Conference on Local Computer Networks (LCN)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/LCN48667.2020.9314823","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 4

Abstract

Data usage control is of utmost importance for federated data analytics across multiple business domains. However, the existing data usage control approaches are limited due to their complexity and inefficiency. This paper proposes an intent-oriented data usage control system for federated data analytics, called IntentKeeper. The system allows users to specify intents for data usage policies and services easily. Thus, it reduces the data sharing complexity for data providers and consumers. Moreover, IntentKeeper enforces preventive and proactive data usage control for better security and efficiency through joint decisions based on policy enforcement and service orchestration. The use case validations for the automotive industry scenario show that IntentKeeper significantly reduces the complexity of policy specification (up to 75% for moderately complex scenarios) compared to the state-of-the-art flow-based approach. Lastly, the experimental results show that the IntentKeeper system provides sufficiently short response times (less than 40ms) with minimal overhead (less than 10ms).
IntentKeeper:联邦数据分析面向意图的数据使用控制
数据使用控制对于跨多个业务域的联邦数据分析至关重要。然而,现有的数据使用控制方法由于其复杂性和低效率而受到限制。本文提出了一种面向意图的联邦数据分析数据使用控制系统IntentKeeper。该系统允许用户方便地指定数据使用策略和服务的意图。因此,它降低了数据提供者和消费者的数据共享复杂性。此外,IntentKeeper通过基于策略实施和服务编排的联合决策,实施预防性和前瞻性数据使用控制,以获得更好的安全性和效率。汽车行业场景的用例验证表明,与最先进的基于流的方法相比,IntentKeeper显著降低了策略规范的复杂性(对于中等复杂的场景,可降低75%)。最后,实验结果表明,IntentKeeper系统以最小的开销(小于10ms)提供了足够短的响应时间(小于40ms)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信