Abstract
Preserving anonymity and confidentiality of transactions has become crucial with widespread of the blockchain technology. Despite of the increased efforts for retaining privacy in blockchain networks, inva-sion attacks are still surfacing. Most of these attacks do not come from outsiders, but from the resident adversarial nodes. Existence of these insider adversaries lead to damaging of an organization's internal network system and information leakage. Consequently, transaction anonymity and confidentiality are compromised. Hence, adversary detection and filtration play a vital role in protecting networks against unforeseen privacy and security threats. Therefore, in this paper, we propose RZee, a cryptographic and statistical privacy preserving model for adversary detection and filtering in blockchain networks. Firstly, RZee exploits zero-knowledge proofs to cryptographically secure the data. Secondly, based on cer-tain identified conditions, RZee captures node behavior and blacklists malicious nodes to restrict those from injecting harmful data into the chain or viewing transactions as they propagate across the network. This adds an additional layer of protecting transactions from unauthorized and malicious intervention. The proposed framework is evaluated based on various privacy attributes as identified by literature. For this evaluation, 4 different types of experiments have been conducted. Further, the comparison of pri-vacy perseverance of RZee with existing benchmark privacy-preserving frameworks is also done. The results depict that performance and privacy preservation in RZee exceeds the rest with an attribute score of 6.774 and a gain margin of 46.5%.(c) 2022 The Author(s). Published by Elsevier B.V. on behalf of King Saud University. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).