An Efficient Location Privacy-Preserving Scheme for Edge Computing Using CLC-to-PKI-Based Heterogeneous Deniable Authentication
Edge computing brings cloud services closer to Internet of Things (IoT) devices by processing data at the edge of the network, reducing latency and bandwidth usage. However, preserving the location privacy of IoT devices is a major challenge due to the heterogeneous, open wireless, and dynamic nature of the edge computing environments. The authentication process in this context may potentially disclose the device’s location, as IoT devices and edge server often rely on different security mechanisms. To address this concern, researchers have proposed various schemes to preserve location privacy. However, these schemes often incur high computational and communication overhead, making them unsuitable for IoT devices, which have limited processing power and storage capacity. In response to this challenge, we present a heterogeneous deniable authentication scheme based on certificateless cryptography (CLC) and public-key infrastructure (PKI), abbreviated as CP-HDA. This scheme enables an IoT device in a CLC to securely transmit messages to an edge server in a PKI. Using the CP-HDA scheme, the edge server can verify the legitimacy of the IoT device but cannot provide evidence to third parties regarding the device’s involvement in communication, thereby preserving the location privacy of the IoT device. It also supports batch verification, which enables faster verification of multiple deniable authenticators. The security of the CP-HDA scheme is formally proven in the random oracle model based on the assumption that the elliptic curve computational Diffie-Hellman problem is hard. In comparison to alternative schemes, our scheme enhances performance by reducing both computational and communication costs.
Authors
- Ikram Ali (ORCID: https://orcid.org/0000-0002-1499-9594)
- Shamsher Ullah (ORCID: https://orcid.org/0009-0003-6399-8461)
- Abdul Wakeel (ORCID: https://orcid.org/0000-0002-9746-6290)
- Baichuan Mo (ORCID: https://orcid.org/0000-0003-0323-0066)
- Jie Chen (ORCID: https://orcid.org/0000-0002-9811-1694)
- Yong Chen (ORCID: https://orcid.org/0000-0003-4580-8655)
- Jianqiang Li (ORCID: https://orcid.org/0000-0002-2208-962X)
Institutions
- University of Electronic Science and Technology of China (CN)
- Shenzhen University (CN)
- ShenZhen People’s Hospital (CN)
- Shenzhen Technology University (CN)
- National University of Sciences and Technology (PK)
Publication Details
- Journal
- IEEE Internet of Things Journal
- Published
- 2025-06-09
- DOI
- https://doi.org/10.1109/jiot.2025.3577148
- Citations
- 1
- Primary Topic
- Privacy-Preserving Technologies in Data
- Type
- article
- Field-Weighted Citation Impact
- 2.48
Funders
- National Natural Science Foundation of China
- Natural Science Foundation of Guangdong Province
- Science, Technology and Innovation Commission of Shenzhen Municipality