ENCRYPT

加密

• 批准号: 10039809
• 金额: $ 60.05万
• 依托单位: THE UNIVERSITY OF MANCHESTER
• 依托单位国家: 英国
• 项目类别: EU-Funded
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=10039809
• 关键词: ENCRYPT

The deluge of big data, accompanied by developments in software and hardware technologies leveraging them, has created new opportunities for research and industry. Europe The main challenges, though, faced by researchers and service providers working with personal data, are stemming from the fact that these data need to be processed in a privacy-preserving way, as they contain sensitive information. Although several technologies have been developed to facilitate the processing of data while preserving privacy, they have not made significant inroads into real use cases, due to several reasons. ENCRYPT will develop a scalable, practical, adaptable privacy preserving framework, allowing researchers and developers to process data stored in federated cross-border data spaces in a GDPR compliant way. Within this framework, a recommendation engine for citizens and end-users will be developed, providing them with personalised suggestions on privacy preserving technologies depending on the sensitivity of data and the accepted trade-off between the degree of security and the overall system performance. The ENCRYPT framework will be designed taking into consideration the needs and preferences of relevant actors, and will be validated in a comprehensive, 3-phase validation campaign, comprising i) in-lab validation tests, ii) use cases provided by consortium partners in three sectors, namely the health sector, the cybersecurity sector, and the finance sector, that include cross-border processing of data, and iii) external use cases including privacy preserving computations on federated medical datasets. ENCRYPT will be realised by a multidisciplinary consortium of 14 partners, comprising six companies (including three SMEs, one start-up, and two enterprises), and eight research institutes/universities, and covering the value chain for privacy-preserving computation technologies.

大数据的泛滥，伴随着利用它们的软件和硬件技术的发展，为研究和工业创造了新的机会。然而，处理个人数据的研究人员和服务提供商面临的主要挑战来自于这些数据需要以保护隐私的方式进行处理，因为它们包含敏感信息。尽管已经开发了几种技术来促进数据处理同时保护隐私，但由于几个原因，它们没有在真实的用例中取得重大进展。ENGINEPT将开发一个可扩展、实用、适应性强的隐私保护框架，使研究人员和开发人员能够以符合GDPR的方式处理存储在联邦跨境数据空间中的数据。在此框架内，将为公民和最终用户开发一个推荐引擎，根据数据的敏感性以及安全程度与整体系统性能之间的可接受权衡，为他们提供有关隐私保护技术的个性化建议。ENCMPT框架的设计将考虑相关参与者的需求和偏好，并将在全面的三阶段验证活动中进行验证，包括i）实验室验证测试，ii）由三个部门的联盟合作伙伴提供的用例，即卫生部门，网络安全部门和金融部门，包括跨境数据处理，以及iii）外部用例，包括对联合医疗数据集的隐私保护计算。ENCIPPT将由14个合作伙伴组成的多学科联盟实现，其中包括6家公司（包括3家中小企业，1家初创企业和2家企业）和8家研究机构/大学，涵盖隐私保护计算技术的价值链。