系统中的隐私算法

P. Yu, O. Kotevska, Tyler Derr
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引用次数: 1

摘要

今天,从健康监测到国家安全基础设施系统,我们面临着数据生成的爆炸式增长。越来越多的系统连接到定期收集数据的互联网上。这些系统共享数据并使用机器学习方法进行智能决策,从而使许多现实世界的应用(例如,自动驾驶汽车、推荐系统和心率监测)从中受益。然而,这些方法容易受到身份盗窃和其他与隐私相关的网络安全攻击。那么,如何在这些情况下有效地保护数据隐私呢?提出将隐私技术集成到现有系统中,并开发更先进的隐私技术来解决多系统连接和数据融合的复杂挑战,需要付出更多的努力。因此,我们在CIKM上引入了系统中的隐私算法(PAS),这为学术研究人员和行业研究人员/从业者提供了一个聚集他们的研究的场所,以努力推进系统中隐私算法这一关键方向的前沿。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
PAS: Privacy Algorithms in Systems
Today we face an explosion of data generation, ranging from health monitoring to national security infrastructure systems. More and more systems are connected to the Internet that collects data at regular time intervals. These systems share data and use machine learning methods for intelligent decisions, which resulted in numerous real-world applications (e.g., autonomous vehicles, recommendation systems, and heart-rate monitoring) that have benefited from it. However, these approaches are prone to identity thief and other privacy related cyber-security attacks. So, how can data privacy be protected efficiently in these scenarios? More dedicated efforts are needed to propose the integration of privacy techniques into existing systems and develop more advanced privacy techniques to address the complex challenges of multi-system connectivity and data fusion. Therefore, we have introduced Privacy Algorithms in Systems (PAS) at CIKM which provides a venue to gather academic researchers and industry researchers/practitioners to present their research in an effort to advance the frontier of this critical direction of privacy algorithms in systems.
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