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.

该项目旨在调查从用户敏感数据学习时隐私，沟通成本和结果准确性之间的权衡。该项目旨在通过开发一种新颖且可广泛的算法框架来为各种机器学习任务设计更快，更准确的算法。该项目的预期结果包括新的理论工具，可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证。个人信息在数据分析管道中的隐私是社会问题。该项目应通过加强这些管道的隐私，同时提高准确性和成本效益来带来重大收益。