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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, invasion 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 …