论文标题
Peopletraffic：协调隐私和流行风险的共同框架
PeopleTraffic: a common framework for harmonizing privacy and epidemic risks
论文作者
论文摘要
PeopleTraffic是一项提议的计划，旨在开发向公共机构，私人公司和民间社会开放的实时，开放型人口密度映射工具，为预防传播提供了一个共同的框架。该系统基于可用2G，3G和4G移动网络运营商的实时人员收集和映射系统，通过采用创新数据匿名算法，从而实施逐个设计的隐私，该算法灵感来自量子信息启发。除了最初是针对协助在COVID-19-Pandemics 2阶段之间平衡社会距离法规的目标之外，PeopleTraffic将对任何感染传播预防事件，例如支持决策者制定战略决策。
PeopleTraffic is a proposed initiative to develop a real-time, open-data population density mapping tool open to public institutions, private companies and the civil society, providing a common framework for infection spreading prevention. The system is based on a real-time people' locations gathering and mapping system from available 2G, 3G and 4G mobile networks operators, enforcing privacy-by-design through the adoption of an innovative data anonymizing algorithm inspired by quantum information de-localizing processes. Besides being originally targeted to help balancing social distancing regulations during the Phase-2 of the COVID-19 pandemics, PeopleTraffic would be beneficial for any infection spreading prevention event, e.g. supporting policy-makers in strategic decision-making.
