TY - JOUR
T1 - Quest
T2 - Privacy-Preserving Monitoring of Network Data: A System for Organizational Response to Pandemics
AU - Sharma, Shantanu
AU - Mehrotra, Sharad
AU - Panwar, Nisha
AU - Venkatasubramanian, Nalini
AU - Gupta, Peeyush
AU - Han, Shanshan
AU - Wang, Guoxi
N1 - Funding Information:
We are very grateful to Dr. Sergei Ilyich Golovatch, Russian Academy of Sciences, for his kindness in translating the reference (in Russian) into English. The present study was supported by Knowledge Innovation Programs of Chinese Academy of Sciences (KZCX2-YW-BR-16), the Na tion al Natural Sciences Foundation of China (NO. 40901036, 31070467), and the Fundamental Research Funds for the Central Universities (NO. 2008-10008).
Publisher Copyright:
© 2008-2012 IEEE.
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
KW - WiFi connectivity data
KW - computation and data privacy
KW - decentralized solution
KW - exposure tracing
UR - http://www.scopus.com/inward/record.url?scp=85128310413&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85128310413&partnerID=8YFLogxK
U2 - 10.1109/TSC.2022.3166802
DO - 10.1109/TSC.2022.3166802
M3 - Article
AN - SCOPUS:85128310413
SN - 1939-1374
VL - 15
SP - 1233
EP - 1250
JO - IEEE Transactions on Services Computing
JF - IEEE Transactions on Services Computing
IS - 3
ER -