TY - JOUR
T1 - Supporting secure dynamic alert zones using searchable encryption and graph embedding
AU - Shaham, Sina
AU - Ghinita, Gabriel
AU - Shahabi, Cyrus
N1 - Publisher Copyright:
© 2023, The Author(s).
PY - 2024/1
Y1 - 2024/1
N2 - Location-based alerts have gained increasing popularity in recent years, whether in the context of healthcare (e.g., COVID-19 contact tracing), marketing (e.g., location-based advertising), or public safety. However, serious privacy concerns arise when location data are used in clear in the process. Several solutions employ searchable encryption (SE) to achieve secure alerts directly on encrypted locations. While doing so preserves privacy, the performance overhead incurred is high. We focus on a prominent SE technique in the public-key setting–hidden vector encryption, and propose a graph embedding technique to encode location data in a way that significantly boosts the performance of processing on ciphertexts. We show that the optimal encoding is NP-hard, and we provide three heuristics that obtain significant performance gains: gray optimizer, multi-seed gray optimizer and scaled gray optimizer. Furthermore, we investigate the more challenging case of dynamic alert zones, where the area of interest changes over time. Our extensive experimental evaluation shows that our solutions can significantly improve computational overhead compared to existing baselines.
AB - Location-based alerts have gained increasing popularity in recent years, whether in the context of healthcare (e.g., COVID-19 contact tracing), marketing (e.g., location-based advertising), or public safety. However, serious privacy concerns arise when location data are used in clear in the process. Several solutions employ searchable encryption (SE) to achieve secure alerts directly on encrypted locations. While doing so preserves privacy, the performance overhead incurred is high. We focus on a prominent SE technique in the public-key setting–hidden vector encryption, and propose a graph embedding technique to encode location data in a way that significantly boosts the performance of processing on ciphertexts. We show that the optimal encoding is NP-hard, and we provide three heuristics that obtain significant performance gains: gray optimizer, multi-seed gray optimizer and scaled gray optimizer. Furthermore, we investigate the more challenging case of dynamic alert zones, where the area of interest changes over time. Our extensive experimental evaluation shows that our solutions can significantly improve computational overhead compared to existing baselines.
KW - Graph embedding
KW - Hidden vector encryption
KW - Secure alert zones
UR - http://www.scopus.com/inward/record.url?scp=85165058549&partnerID=8YFLogxK
U2 - 10.1007/s00778-023-00803-2
DO - 10.1007/s00778-023-00803-2
M3 - Article
AN - SCOPUS:85165058549
SN - 1066-8888
VL - 33
SP - 185
EP - 206
JO - VLDB Journal
JF - VLDB Journal
IS - 1
ER -