PriPeARL：LinkedIn的隐私保护分析和报告框架
PriPeARL: A Framework for Privacy-Preserving Analytics and Reporting at LinkedIn
摘要：
保护用户隐私是网络规模分析和报告应用程序的一项关键要求，鉴于最近的数据泄露和GDPR等新法规，保护用户隐私已成为新的关注焦点。我们专注于以隐私保护的方式计算强大，可靠的分析，同时满足产品要求的问题。我们提出了PriPeARL，一个隐私保护分析和报告的框架，灵感来自差分隐私。我们描述了整体设计和架构，以及关键的建模组件，重点关注与隐私，覆盖范围，实用程序和一致性相关的独特挑战。我们在LinkedIn的广告分析和报告的背景下进行了一项实验研究，从而展示了隐私和实用需求之间的权衡，以及隐私保护机制对现实世界数据的适用性。我们还强调了从LinkedIn系统的生产部署中吸取的经验教训。
英文摘要：
Preserving privacy of users is a key requirement of web-scale analytics and reporting applications, and has witnessed a renewed focus in light of recent data breaches and new regulations such as GDPR. We focus on the problem of computing robust, reliable analytics in a privacy-preserving manner, while satisfying product requirements. We present PriPeARL, a framework for privacy-preserving analytics and reporting, inspired by differential privacy. We describe the overall design and architecture, and the key modeling components, focusing on the unique challenges associated with privacy, coverage, utility, and consistency. We perform an experimental study in the context of ads analytics and reporting at LinkedIn, thereby demonstrating the tradeoffs between privacy and utility needs, and the applicability of privacy-preserving mechanisms to real-world data. We also highlight the lessons learned from the production deployment of our system at LinkedIn.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
