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Fully Homomorphic Encryption (FHE) in the realm of deep neural network (DNN) encrypted inference represents a pivotal advancement in privacy-preserving machine learning. This technology allows users to securely access DNN inference services hosted on remote servers without compromising their personal privacy. Given its wide range of potential applications, FHE has garnered significant research attention. Despite its rapid progress, FHE still faces considerable challenges, particularly the high computational resource demands. Moreover, varying configurations of FHE schemes can lead to notable differences in performance, whether in terms of efficiency or inference accuracy, making it difficult to strike an optimal balance tailored to specific application requirements.To tackle this challenge, we introduce a new approach that simulates FHE-induced errors to assess the impact of different FHE architectures and parameter configurations on encrypted inference during the model testing phase. Our simulation framework enables efficient approximation of homomorphic computation outcomes on a DNN model, specifically for third-generation FHEs, without the need for executing the complete homomorphic process. This method significantly streamlines the research process for optimizing parameters based on specific application needs. We validate the effectiveness of our approach through extensive performance benchmarking across a variety of experimental settings.