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

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

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.

Original languageEnglish
Title of host publicationProceedings - 2022 IEEE International Conference on Big Data, Big Data 2022
EditorsShusaku Tsumoto, Yukio Ohsawa, Lei Chen, Dirk Van den Poel, Xiaohua Hu, Yoichi Motomura, Takuya Takagi, Lingfei Wu, Ying Xie, Akihiro Abe, Vijay Raghavan
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1445-1454
Number of pages10
ISBN (Electronic)9781665480451
DOIs
StatePublished - 2022
Event2022 IEEE International Conference on Big Data, Big Data 2022 - Osaka, Japan
Duration: 17 Dec 202220 Dec 2022

Publication series

NameProceedings - 2022 IEEE International Conference on Big Data, Big Data 2022

Conference

Conference2022 IEEE International Conference on Big Data, Big Data 2022
Country/TerritoryJapan
CityOsaka
Period17/12/2220/12/22

Keywords

  • Hilbert Curve
  • Location Privacy
  • Searchable Encryption

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