Search results for "Security and privacy"
showing 3 items of 3 documents
Discovery privacy threats via device de-anonymization in LoRaWAN
2021
LoRaWAN (Long Range WAN) is one of the well-known emerging technologies for the Internet of Things (IoT). Many IoT applications involve simple devices that transmit their data toward network gateways or access points that, in their turn, redirect data to application servers. While several security issues have been addressed in the LoRaWAN specification v1.1, there are still some aspects that may undermine privacy and security of the interconnected IoT devices. In this paper, we tackle a privacy aspect related to LoRaWAN device identity. The proposed approach, by monitoring the network traffic in LoRaWAN, is able to derive, in a probabilistic way, the unique identifier of the IoT device from…
An Efficient and Secure Multidimensional Data Aggregation for Fog-Computing-Based Smart Grid
2021
International audience; The secure multidimensional data aggregation (MDA) has been widely investigated in smart grid for smart cities. However, previous proposals use heavy computation operations either to encrypt or to decrypt the multidimensional data. Moreover, previous fault-tolerant mechanisms lead to an important computation cost, and also a high communication cost when considering a separate identification phase. In this article, we propose an efficient and secure MDA scheme, named ESMA. Unlike existing schemes, the multidimensional data in ESMA are structured and encrypted into a single Paillier ciphertext and thereafter, the data are efficiently decrypted. For privacy preserving, …
On-Demand Security Framework for 5GB Vehicular Networks
2023
Building accurate Machine Learning (ML) at-tack detection models for 5G and Beyond (5GB) vehicular networks requires collaboration between Vehicle-to-Everything (V2X) nodes. However, while operating collaboratively, ensuring the ML model’s security and data privacy is challenging. To this end, this article proposes a secure and privacy-preservation on-demand framework for building attack-detection ML models for 5GB vehicular networks. The proposed framework emerged from combining 5GB technologies, namely, Federated Learning (FL), blockchain, and smart contracts to ensure fair and trustedinteractions between FL servers (edge nodes) with FL workers (vehicles). Moreover, it also provides an ef…