RAPID: An Organizational Scale Approach to Privacy-Enabled Contact Tracing in COVID-19

RAPID：在 COVID-19 中启用隐私的组织规模接触者追踪方法

• 批准号: 2032525
• 负责人: Sharad Mehrotra
• 金额: $ 10万
• 依托单位: University of California-Irvine
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-06-15 至 2022-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2032525&HistoricalAwards=false
• 关键词: RAPID; Organizational; Scale; Approach; Privacy

Contact tracing has emerged as a key mitigation strategy to prevent the spread of pandemics such as COVID-19. Recently, several efforts have been initiated to track individuals, their movements, and interactions using technologies such as Bluetooth beacons, cellular data records, and smartphone applications. Such solutions can be intrusive, potentially violate individual privacy rights and are often subject to regulations that mandate the need for opt-in policies to gather and use personal information which, as several studies have shown, limits their adoption. This project takes a novel approach to empower organizations to mitigate spread of COVID-19 at their premises by exploiting connection events between mobile devices carried by individuals and the Wi-Fi infrastructure. There are several advantages of the planned approach. First, it takes an organizational perspective and is intended to help organizations, small and large, keep employees safe and ensure safety on their premises by exploiting network data (already being generated by their network infrastructures). Second, it is decentralized, i.e., instead of empowering/trusting a small number of organizations such as mobile OS companies, it empowers organizations to assume joint responsibility to implement safety measures at their premises. Third, it offers a fully privacy-preserving solution based on computationally and informationally secure cryptography with strong security properties guaranteeing privacy of individuals, including those who might be exposed or carriers. This will prevent misuse of the data collected by any entity against the will of the individuals. Fourth, it is based on connectivity events already generated by existing Wi-Fi infrastructure and does not require users of the network to either download any application and/or give explicit permissions (which is known to limit adoption). Finally, it offers a path to implement technology not just for contact tracing but empowers organizations with awareness about effectiveness of their policies/strategies such as social distancing, disinfecting/cleaning schedules, etc. The planned approach is built upon several innovations including (a) new algorithms for cleaning noisy Wi-Fi connectivity data to develop models of occupancy, (b) new cryptographic solutions to implement privacy-preserving data analytics and queries over encrypted Wi-Fi connectivity data (collected from mobile devices), to generate a range of information -- e.g., level of adherence to social distancing policies, flow of people in spaces, and exposure hotspots, (c) design of a range of COVID-19 relevant applications that help organizations ensure safety of individuals on their premises. Such applications include publicly accessible organizational portals/dashboards and subscription-based alerting technologies developed in concert with stakeholder (e.g., UCI campus administration). Deployment and testing of the solution will be done at campus scale, particularly focusing on how the technology can empower the university leadership to determine strategies for reopening research labs while maintaining health and safety of all involved and follow the applicable mandates of the public health authorities. It is expected that the project will serve as a vehicle to inform and educate both individuals and scientists about virus transmission and spread and will contribute to the development of processes and actions at the organizational level to mitigate spread of COVID-19.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

接触者追踪已成为防止COVID-19等大流行病传播的一项关键缓解战略。最近，人们开始尝试使用蓝牙信标、蜂窝数据记录和智能手机应用程序等技术来跟踪个人、他们的活动和互动。此类解决方案可能具有侵入性，可能侵犯个人隐私权，而且往往受到法规的约束，这些法规要求收集和使用个人信息需要选择加入政策，正如几项研究表明的那样，这限制了它们的采用。该项目采用了一种新颖的方法，通过利用个人携带的移动设备与Wi-Fi基础设施之间的连接事件，使组织能够减轻COVID-19在其场所的传播。有计划的方法有几个优点。首先，它从组织的角度出发，旨在帮助组织，无论大小，通过利用网络数据（已经由其网络基础设施生成）来保护员工的安全并确保其场所的安全。其次，它是分散的，即不是授权/信任少数组织（如移动操作系统公司），而是授权组织承担在其场所实施安全措施的共同责任。第三，它提供了一个基于计算和信息安全加密的完全隐私保护解决方案，具有强大的安全属性，保证了个人的隐私，包括那些可能被暴露的人或运营商。这将防止任何实体违反个人意愿滥用所收集的数据。第四，它基于现有Wi-Fi基础设施已经产生的连接事件，不需要网络用户下载任何应用程序和/或给予明确的许可（这是已知的限制采用）。最后，它提供了一种实施技术的途径，不仅可以用于追踪接触者，还可以使组织意识到其政策/战略的有效性，如保持社交距离、消毒/清洁时间表等。计划中的方法建立在几个创新的基础上，包括(a)清理嘈杂的Wi-Fi连接数据的新算法，以开发占用模型；(b)新的加密解决方案，以实现保护隐私的数据分析和对加密的Wi-Fi连接数据（从移动设备收集）的查询，以生成一系列信息，例如，遵守社交距离政策的程度、空间中的人员流动和暴露热点。(c)设计一系列与COVID-19相关的应用程序，帮助组织确保个人在其场所的安全。这些应用程序包括可公开访问的组织门户/仪表板，以及与利益相关者（例如UCI校园管理）共同开发的基于订阅的警报技术。该解决方案的部署和测试将在校园规模上进行，特别是侧重于该技术如何使大学领导层能够确定重新开放研究实验室的战略，同时保持所有相关人员的健康和安全，并遵循公共卫生当局的适用规定。预计该项目将成为一种工具，向个人和科学家提供有关病毒传播和传播的信息和教育，并将有助于制定组织层面的流程和行动，以减轻COVID-19的传播。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Sieve: a middleware approach to scalable access control for database management systems

Sieve：一种用于数据库管理系统可扩展访问控制的中间件方法

• DOI: 10.14778/3407790.3407835
• 发表时间: 2020
• 期刊: Proceedings of the VLDB Endowment
• 影响因子: 2.5
• 作者: Pappachan, Primal;Yus, Roberto;Mehrotra, Sharad;Freytag, Johann-Christoph
• 通讯作者: Freytag, Johann-Christoph

MIDE: accuracy aware minimally invasive data exploration for decision support

MIDE：用于决策支持的准确性感知微创数据探索

• DOI: 10.14778/3551793.3551821
• 发表时间: 2022
• 期刊: Proceedings of the VLDB Endowment
• 影响因子: 2.5
• 作者: Ghayyur, Sameera;Ghosh, Dhrubajyoti;He, Xi;Mehrotra, Sharad
• 通讯作者: Mehrotra, Sharad

Supporting Complex Query Time Enrichment For Analytics

支持复杂的查询时间丰富分析

• 发表时间: 2023
• 期刊: 26th International Conference on Extending Database Technology (EDBT
• 作者: Ghosh, Dhrubajyoti;Gupta, Peeyush;Mehrotra, Sharad;Sharma, Shantanu
• 通讯作者: Sharma, Shantanu

Don't Be a Tattle-Tale: Preventing Leakages through Data Dependencies on Access Control Protected Data

• DOI: 10.14778/3551793.3551805
• 发表时间: 2022-07
• 期刊: Proc. VLDB Endow.
• 作者: Primal Pappachan;Shufan Zhang;Xi He;S. Mehrotra

Quest: Privacy-Preserving Monitoring of Network Data: A System for Organizational Response to Pandemics

• DOI: 10.1109/tsc.2022.3166802
• 发表时间: 2022-05
• 期刊: IEEE Transactions on Services Computing
• 影响因子: 8.1
• 作者: Shantanu Sharma;S. Mehrotra;Nisha Panwar;N. Venkatasubramanian;Peeyush Gupta;Shanshan Han;Guoxi Wang-Guoxi