M.Thangavel, A.M.Kalpana, S.Ananth 03 09 Privacypreserving data collection is essential for IoT environments where massive volumes of keyvalue data are continuously generated and transmitted across distributed devices Local Differential Privacy LDP provides protection without requiring trusted data collectors but conventional mechanisms often introduce substantial estimation errors and reduced data utility particularly for composite keyvalue structures This work addresses these limitations through an accuracyoriented privacypreserving collection framework using a keyvalue dataset containing categorical keys and associated values collected over a defined observation period The data is preprocessed by removing missing and duplicate records validating keyvalue pairs encoding categorical attributes and applying normalization where required The implemented models include Randomized Response Generalized Randomized Response Optimized Unary Encoding and proposed hybrid mechanisms combining key and value perturbation with samplingbased processing Performance is evaluated using privacy level hit rate estimation variance precision and utility preservation Among the evaluated approaches the proposed hybrid keyvalue perturbation mechanism achieves the highest hit rate and the lowest estimation variance demonstrating improved estimation reliability under privacy constraints The results indicate that coordinated sampling and perturbation can provide stronger privacy protection while maintaining high data utility offering an effective approach for accurate and privacypreserving keyvalue data collection in IoT environments Copyright (c) Journal of Science Engineering Technology and Management Science. All rights reserved 2026