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Data/AI Secure-by-Design Orchestration Platform (DAISOR)

数据/人工智能安全设计编排平台 (DAISOR)

基本信息

批准号：
10077592
负责人：
金额：
$ 3.2万
依托单位：
UNIVERSITY OF ESSEX
依托单位国家：
英国
项目类别：
Collaborative R&D
财政年份：
2023
资助国家：
英国
起止时间：
2023 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=10077592
关键词：
Data AI Secure Design Orchestration

项目摘要

The transition of business to digital operations, known as Digital transformation (DX), is leveraging data and AI as cloud services. Yet, existing cloud technologies do not support businesses to maintain the privacy and security of their data and AI, particularly when using multiple clouds.Our innovation will give businesses, particularly those of limited cloud resources such as SMEs, the ability to preserve the privacy and security of their data and AI workflows in a flexible manner, while better utilising their limited resources in combination with those from subscription(s) to external clouds. DAISOR is a user-friendly web service that allows developers (particularly data scientists) to focus on what matters to them in defining the privacy and security semantics they wish to maintain along with the desired behaviour of their AI application, without having to deal with the cloud orchestration tasks. Unlike existing technologies, through novel orchestration functions and interaction with multiple clouds, DAISOR provides a secure-by-design deployment of AI applications and their needed data, without compromising privacy requirements and with security checks tailored to business intents. Our technology will enable privacy-preservant and secure (re)usability even of sensitive data, to ensure adequate supply of needed data to AI-assisted digital ecosystems.

业务向数字化运营的过渡，即数字化转型（DX），正在利用数据和人工智能作为云服务。然而，现有的云技术并不支持企业维护其数据和人工智能的隐私和安全性，特别是在使用多云时。我们的创新将使企业，特别是那些云资源有限的企业，如中小企业，能够以灵活的方式保护其数据和人工智能工作流程的隐私和安全性，同时更好地利用其有限的资源以及来自订阅到外部云的资源。DAISOR是一个用户友好的Web服务，允许开发人员（特别是数据科学家）专注于定义他们希望维护的隐私和安全语义沿着AI应用程序的期望行为，而不必处理云编排任务。与现有技术不同，通过新颖的编排功能和与多个云的交互，DAISOR提供了人工智能应用程序及其所需数据的安全设计部署，而不会影响隐私要求，并根据业务意图量身定制安全检查。我们的技术将实现隐私保护和安全（可重复）使用，即使是敏感数据，以确保向人工智能辅助的数字生态系统提供足够的所需数据。