Effective and efficient protection of personal privacy in big personal data

有效、高效地保护个人大数据中的个人隐私

• 批准号: DP190101893
• 负责人: Prof Jinjun Chen
• 金额: $ 26.63万
• 依托单位: Swinburne University of Technology
• 依托单位国家: 澳大利亚
• 项目类别: Discovery Projects
• 财政年份: 2019
• 资助国家: 澳大利亚
• 起止时间: 2019-01-01 至 2023-12-11
• 项目状态: 已结题
• 来源: https://dataportal.arc.gov.au/RGS/Web/Grants/DP190101893
• 关键词: Effective; efficient; protection; personal; privacy

Personal privacy protection is becoming increasingly important as personal data is increasingly being hosted in cloud servers, accumulating as big personal data. This project aims to develop innovative solutions for effective and efficient to address the issue of protection of personal privacy. Current approaches are neither effective nor efficient, and lack robustness. The project is expected to enhance theoretical foundation of personal privacy protection in big data and cloud, and deliver an effective and efficient personal privacy protection framework with associated algorithms and prototype. These outcomes will help to protect fast-growing privacy sensitive personal data hosting and applications on cloud servers.

随着个人数据越来越多地托管在云服务器上，积累成大的个人数据，个人隐私保护变得越来越重要。该项目旨在开发创新的解决方案，以有效和高效地解决个人隐私保护问题。目前的方法既不有效也不高效，而且缺乏稳健性。该项目旨在加强大数据和云环境下个人隐私保护的理论基础，并提供一个有效和高效的个人隐私保护框架及其相关算法和原型。这些成果将有助于保护云服务器上快速增长的隐私敏感个人数据托管和应用程序。