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.

该项目旨在研究从用户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来指导数据驱动决策系统的设计并严格分析其性能和隐私保证。数据分析管道中个人信息的隐私是一个关键的社会问题。该项目应该通过加强这些管道中的隐私性同时提高准确性和成本效率来带来显着的效益。