持续监控下差分隐私保护

近年来,随着信息技术的发展及物联网技术的兴起,出现了越来越多的持续监控应用场景,如智能交通实时监控、疾病实时监控、智能基础设施应用等.在这些场景中,如何对参与者持续分享的数据进行隐私保护面临重大挑战.差分隐私是一种严格和可证明的隐私定义,早期差分隐私研究大都基于一个大规模、静态的数据集做一次性的计算和发布.而持续监控下差分隐私保护需对动态数据做持续计算和发布.目前,持续监控下差分隐私保护是差分隐私领域新的研究热点之一.对持续监控下差分隐私保护的已有研究成果进行总结.首先,对该场景下差分隐私保护模型进行阐述;然后,重点介绍了持续监控下满足event级、user级和w-event级隐私保护的实现方案.在对已有研究成果深入对比分析的基础上,指出了持续监控下差分隐私保护的未来研究方向.

Differential Privacy under Continual Observation

With the development of information technologies and Internet of things(IoT)technologies,there are more and more scenarios under continual monitoring,such as transportation monitoring,disease monitoring,smart infrastructure etc.In these scenarios,how to protect the privacy of continuous sharing data is facing major challenges.Differential privacy is arigorous and provable privacy definition.Earlier research on differential privacy has focused on“one-shot”release on a static dataset.However,differential privacy under continual observation focuses on the continuous computationon the dynamic dataset.Now it has become one of the research hotspots.This study surveys the state-of-the-art techniqueson differential privacy under continual observation,and focuses on summarizing existing schemes that provide event-levelprivacy,user-levelprivacy,and w-event privacy.Following a comprehensive comparison and analysis of existing techniques,further research prospectsare put forward.