Shahriar Badsha

TL;DR: This paper focuses and briefly discusses on cybersecurity data science, where the data is being gathered from relevant cybersecurity sources, and the analytics complement the latest data-driven patterns for providing more effective security solutions.

TL;DR: This work proposes a privacy-preserving recommender system using homomorphic encryption, by which the system can provide recommendations without knowing the actual ratings, based on the ElGamal cryptosystem by which both addition and multiplication of plaintexts can be performed.

TL;DR: This paper proposes a new technique called Proof of Driving (PoD), to randomize the selection of honest miners for generating the blocks efficiently for blockchain-based VANET applications, and introduces a filtering technique based on Service Standard Score of the vehicular miner nodes to detect and eliminate the malicious nodes.

TL;DR: This work proposes a privacy preserving user-based CF technique based on homomorphic encryption, which is capable of determining similarities among users followed by generating recommendations without revealing any private information.

TL;DR: A new generation of Fog-Based Context-Aware Access Control (FB-CAAC) framework is proposed, combining the benefits of the cloud, IoT and context-aware computing; and ensuring proper access control and security at the edge of the end-devices.