Trading Privacy, Bandwidth and Accuracy in Algorithmic Machine Learning

算法机器学习中的隐私、带宽和准确性的交易

• 批准号: DE230101329
• 负责人: Dr Clément Canonne
• 金额: $ 30.46万
• 依托单位: The University of Sydney
• 依托单位国家: 澳大利亚
• 项目类别: Discovery Early Career Researcher Award
• 财政年份: 2023
• 资助国家: 澳大利亚
• 起止时间: 2023-01-01 至 2025-12-31
• 项目状态: 未结题
• 来源: https://dataportal.arc.gov.au/RGS/Web/Grants/DE230101329
• 关键词: Trading; Privacy; Bandwidth; Accuracy; Algorithmic

This project aims to investigate the trade-offs between privacy, communication costs and accuracy of results when learning from users' sensitive data. The project intends to design faster and more accurate algorithms for a wide range of machine learning tasks by developing a novel and widely-applicable algorithmic framework. Expected outcomes of this project include new theoretical tools to guide the design of data-driven decision systems and rigorously analyse their performance and privacy guarantees. Privacy of individuals' information in data analytics pipelines is a key societal concern. This project should lead to significant benefits by strengthening privacy in these pipelines while also improving accuracy and cost-efficiency.

这个项目旨在调查在从用户的敏感数据中学习时，隐私、通信成本和结果准确性之间的权衡。该项目旨在通过开发一个新颖且广泛适用的算法框架，为广泛的机器学习任务设计更快、更准确的算法。该项目的预期成果包括新的理论工具，以指导数据驱动的决策系统的设计，并严格分析其性能和隐私保障。数据分析管道中个人信息的隐私是一个关键的社会问题。该项目通过加强这些管道的私密性，同时提高准确性和成本效益，应该会带来显著的好处。