Location-based Alert System Using Searchable Encryption with Hilbert Curve Encoding

Po Wei Harn, Sai Deepthi Yeddula, Libo Sun, Min Te Sun, Wei Shinn Ku

研究成果: 書貢獻/報告類型會議論文篇章同行評審

1 引文 斯高帕斯(Scopus)

摘要

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.

原文???core.languages.en_GB???
主出版物標題Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022
編輯Shusaku Tsumoto, Yukio Ohsawa, Lei Chen, Dirk Van den Poel, Xiaohua Hu, Yoichi Motomura, Takuya Takagi, Lingfei Wu, Ying Xie, Akihiro Abe, Vijay Raghavan
發行者Institute of Electrical and Electronics Engineers Inc.
頁面1445-1454
頁數10
ISBN(電子)9781665480451
DOIs
出版狀態已出版 - 2022
事件2022 IEEE International Conference on Big Data, Big Data 2022 - Osaka, Japan
持續時間: 17 12月 202220 12月 2022

出版系列

名字Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022

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???event.eventtypes.event.conference???2022 IEEE International Conference on Big Data, Big Data 2022
國家/地區Japan
城市Osaka
期間17/12/2220/12/22

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