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
基本信息
- 批准号:DE230101329
- 负责人:
- 金额:$ 30.46万
- 依托单位:
- 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:
- 关键词:
项目摘要
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种新颖且广泛适用的算法框架,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。数据分析管道中的个人信息隐私是一个关键的社会问题。该项目将通过加强这些管道中的隐私,同时提高准确性和成本效益,带来显著的好处。
项目成果
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