Enhancing data integrity in opportunistic mobile social network: Leveraging Berkle Tree and secure data routing against attacks

In Opportunistic Mobile Social Networks (OMSNs), ensuring data integrity is crucial. The anonymous and opportunistic nature of node communication makes these networks vulnerable to data integrity attacks. The existing literature identified significant shortcomings in effectively addressing data integrity attacks with high efficiency and accuracy. This paper addresses these issues by proposing the "Berkle Tree", a novel data structure designed to mitigate data integrity attacks in OMSNs. The Berkle Tree leverages the EvolvedBloom filter, which is a variant of the bloom filter with a negligible False Positive Rate (FPR). The key contributions of this study include i) an innovative application of EvolvedBloom for membership testing and Berkle Tree root validation, and ii) comparative analysis with existing data structures like Merkle and Verkle Trees. The Berkle Tree demonstrates superior performance, reducing tree generation and integrity validation times and leading to substantial computational cost reductions of 79.50 % and 90.57 %, respectively. The proposed method integrates the Berkle Tree into OMSN routing models and evaluates performance against Packet Drop, Modification, and Fake Attacks (PDA, PMA, PFA). Results show average Malicious Node Detection Accuracy of 98.2 %, 85.2 %, and 94.4 %; Malicious Path Detection Accuracy of 98.6 %, 86.6 %, and 90.2 %; Malicious Data Detection Accuracy of 98.4 %, 80.2 %, and 93.4 %; and False Negative Rates of 1.8 %, 14.8 %, and 5.6 % for PDA, PMA, and PFA, respectively. The major findings demonstrate that the proposed approach significantly improves OMSN routing models by reducing Packet Dropping, Modifying, and Faking Rates by 48.62 %, 28.99 %, and 31.2 %, respectively. Compared to existing methods, the Berkle Tree achieves a substantial reduction in filter size by approximately 25 %–40 %, while maintaining a negligible FPR. These advancements contribute to the state-of-the-art of OMSNs by providing robust solutions for data integrity with significant implications for enhancing security and trustworthiness in OMSNs.