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A secure location-based alert system with tunable privacy-performance trade-off
#MMPMID32837253
Ghinita G
; Nguyen K
; Maruseac M
; Shahabi C
Geoinformatica
2020[]; 24
(4
): 951-985
PMID32837253
show ga
Monitoring location updates from mobile users has important applications in many
areas, ranging from public health (e.g., COVID-19 contact tracing) and national
security to social networks and advertising. However, sensitive information can
be derived from movement patterns, thus protecting the privacy of mobile users is
a major concern. Users may only be willing to disclose their locations when some
condition is met, for instance in proximity of a disaster area or an event of
interest. Currently, such functionality can be achieved using searchable
encryption. Such cryptographic primitives provide provable guarantees for
privacy, and allow decryption only when the location satisfies some predicate.
Nevertheless, they rely on expensive pairing-based cryptography (PBC), of which
direct application to the domain of location updates leads to impractical
solutions. We propose secure and efficient techniques for private processing of
location updates that complement the use of PBC and lead to significant gains in
performance by reducing the amount of required pairing operations. We implement
two optimizations that further improve performance: materialization of results to
expensive mathematical operations, and parallelization. We also propose an
heuristic that brings down the computational overhead through enlarging an alert
zone by a small factor (given as system parameter), therefore trading off a small
and controlled amount of privacy for significant performance gains. Extensive
experimental results show that the proposed techniques significantly improve
performance compared to the baseline, and reduce the searchable encryption
overhead to a level that is practical in a computing environment with reasonable
resources, such as the cloud.
free
free
free
