@inproceedings{4523855684eb4d7bbc9e8266cd91b230,
title = "Location-based Alert System Using Searchable Encryption with Hilbert Curve Encoding",
abstract = "The location-based alert system plays a primary factor on determining who is at risk during an emergency, such as a war zone in Ukraine. While users are willing to reveal their locations in exchange for timely alert in those situations, there is no guarantee that their private information does not fall into the wrong hands. For example, a soldier may be killed if his movement pattern is known by the enemy. One resolution to this issue is to encrypt the location information by trusted authority public key before it is transmitted. This approach provides location privacy and allows decryption only when the recipient's location satisfies a certain predicate. However, the encryption itself may still be compromised if the location encoding is leaked. In this paper, we propose a Hilbert Curve Encoding which encrypts the user's message along with her locations for private processing with the trusted authority. We further propose a hybrid HNGM-N Encoding which combines the Hilbert Curve Encoding and Gray Encoding. HNGM-N has the proprieties of a Hilbert Curve Encoding in its identifier and a Hamming distance of 1 between neighboring cells in a subgrid. As a consequence, the proposed HNGM-N is less likely to leak neighboring cell identifier than Gray Encoding under random guessing attacks. Extensive experiment results show that our encoding methods are better than Hierarchical Encoding and comparable to Gray Encoding in terms of user response time, token remaining percentage, and execution time.",
keywords = "Hilbert Curve, Location Privacy, Searchable Encryption",
author = "Harn, {Po Wei} and Yeddula, {Sai Deepthi} and Libo Sun and Sun, {Min Te} and Ku, {Wei Shinn}",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 2022 IEEE International Conference on Big Data, Big Data 2022 ; Conference date: 17-12-2022 Through 20-12-2022",
year = "2022",
doi = "10.1109/BigData55660.2022.10020428",
language = "???core.languages.en_GB???",
series = "Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1445--1454",
editor = "Shusaku Tsumoto and Yukio Ohsawa and Lei Chen and {Van den Poel}, Dirk and Xiaohua Hu and Yoichi Motomura and Takuya Takagi and Lingfei Wu and Ying Xie and Akihiro Abe and Vijay Raghavan",
booktitle = "Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022",
}