Quest : Privacy-Preserving Monitoring of Network Data: A System for Organizational Response to Pandemics
IEEE Transactions on Services Computing(2022)
摘要
Most modern organizations today support network infrastructure to provide ubiquitous network coverage at their premises. Such a network infrastructure consisting of a set of access points deployed at different locations in buildings can be used to support coarse-level localization of individuals, who connect to the infrastructure using their mobile devices. This paper describes a system, entitled
Quest
that supports a variety of applications (
e
.
g
., identifying hotspot regions, finding people who are potentially exposed to a condition such as COVID-19, occupancy count of a region/floor/building) based on network data to empower organizations to maintain safety at their workplace/premises.
Quest
builds the above functionalities while fully protecting the privacy of individuals.
Quest
incorporates computationally- and information-theoretically-secure protocols that prevent adversaries from gaining knowledge of an individual's location history (based on WiFi data). We describe the architecture, design choices, and implementation of the proposed security/privacy techniques in
Quest
. We, also, validate the practicality of
Quest
and evaluate it thoroughly via an actual campus-scale deployment at our organization over a very large dataset of over 50M rows.
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关键词
WiFi connectivity data,computation and data privacy,exposure tracing,decentralized solution
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