| Title | Certificateless Cryptographic Protocols for Efficient Drone-Based Smart City Applications |
|---|---|
| ID_Doc | 37770 |
| Authors | Won, J; Seo, SH; Bertino, E |
| Title | Certificateless Cryptographic Protocols for Efficient Drone-Based Smart City Applications |
| Year | 2017 |
| Published | |
| Abstract | Smart cities aim to improve the quality of urban services and their energy efficiency by utilizing information and communication technologies. In such context, drones can be utilized to support various services, such as traffic monitoring, search/rescue, and surveillance, by communicating with many different smart objects like sensors. Securing such communications is crucial to making correct decisions and requires efficient cryptographic protocols. However, the design of such protocols must consider: 1) the mobility and the limited battery of drones and 2) the constrained resources of smart objects. In this paper, a suite of cryptographic protocols is presented to deal with three different communication scenarios: one-to-one, one-to-many, and many-to-one. For one-to-one, we propose an efficient Certificateless Signcryption Tag Key Encapsulation Mechanism (eCLSC-TKEM) that supports authenticated key agreement, non-repudiation, and user revocation. eCLSC-TKEM reduces the time required to establish a shared key between a drone and a smart object by minimizing the computational overhead at the smart object. For one-to-many, we propose a Certificateless Multi-Recipient Encryption Scheme (CL-MRES) by which a drone can efficiently send privacy-sensitive data to multiple smart objects. For many-to-one, we present a Certificateless Data Aggregation (CLDA) protocol, which allows drones to efficiently collect data from hundreds of smart objects. Also, for efficiency, we propose a dual channel strategy that allows many smart objects to concurrently execute our protocols. We evaluate eCLSC-TKEM via a smart parking management test-bed. Also, we have implemented CL-MRES and CLDA on a board with a graphics processing unit (GPU) and show their GPU-accelerated performance. |
| https://doi.org/10.1109/access.2017.2684128 |
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