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

Shantanu Sharma, Sharad Mehrotra, Nisha Panwar, Nalini Venkatasubramanian, Peeyush Gupta, Shanshan Han, Guoxi Wang

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)1233-1250
Number of pages18
JournalIEEE Transactions on Services Computing
Issue number3
StatePublished - 2022

All Science Journal Classification (ASJC) codes

  • Hardware and Architecture
  • Computer Science Applications
  • Computer Networks and Communications
  • Information Systems and Management


  • WiFi connectivity data
  • computation and data privacy
  • decentralized solution
  • exposure tracing


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