构建兼顾隐私、可扩展与实用性的跨设备联邦分析系统
PAPAYA Federated Analytics Stack: Engineering Privacy, Scalability and Practicality
- 结合可信执行环境与设备端计算优化，实现大规模设备上安全数据分析
- 支持统计与监控类分析任务，显著提升隐私保护下的数据可用性
- 适合需要高隐私合规的工业级数据分析场景
跨设备联邦分析（FA）是一种分布式计算范式，旨在从用户设备本地数据中回答分析查询并提取洞察。通过设备端计算与其他隐私安全措施，仅传输极少数据，实现高水平数据保护。尽管FA应用广泛，现有系统受限于准确率下降、数据分析灵活性不足以及难以有效扩展。本文提出一种融合隐私、可扩展性与实用性的联邦分析系统，利用可信执行环境（TEEs）并优化设备端计算资源，实现大规模设备上的联邦数据处理，同时确保强而可验证的隐私保障。系统聚焦于联邦分析（统计与监控），不同于面向机器学习任务的联邦学习系统，并明确指出两者关键差异。
原文摘要 · Abstract (English)
Cross-device Federated Analytics (FA) is a distributed computation paradigm designed to answer analytics queries about and derive insights from data held locally on users' devices. On-device computations combined with other privacy and security measures ensure that only minimal data is transmitted off-device, achieving a high standard of data protection. Despite FA's broad relevance, the applicability of existing FA systems is limited by compromised accuracy; lack of flexibility for data analytics; and an inability to scale effectively. In this paper, we describe our approach to combine privacy, scalability, and practicality to build and deploy a system that overcomes these limitations. Our FA system leverages trusted execution environments (TEEs) and optimizes the use of on-device computing resources to facilitate federated data processing across large fleets of devices, while ensuring robust, defensible, and verifiable privacy safeguards. We focus on federated analytics (statistics and monitoring), in contrast to systems for federated learning (ML workloads), and we flag the key differences.
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