PeopleTraffic：协调隐私和流行病风险的通用框架
PeopleTraffic: a common framework for harmonizing privacy and epidemic risks
摘要：
PeopleTraffic是一项拟议的倡议，旨在开发一个向公共机构、私营公司和民间社会开放的实时、开放数据人口密度绘图工具，为预防感染传播提供一个共同框架。该系统基于现有2G、3G和4G移动网络运营商的实时人员位置收集和映射系统，通过采用受量子信息去本地化过程启发的创新数据匿名算法来实施隐私设计。除了最初旨在帮助平衡COVID-19大流行病第二阶段期间的社交距离法规外，PeopleTraffic还将有利于任何感染传播预防事件，例如支持政策制定者进行战略决策。
英文摘要：
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.
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。
