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Nowadays, the massive amount of are data produced in various sectors like health care, insurance, banking stock market, etc. The dissemination of the data for mining and analysis enables to gain valuable knowledge to have remarkable social and economic development. The greatest challenge in various big data applications is privacy. The sensitive information may cause financial loss or reputation of the individual. Hence, various techniques are proposed to guard against the privacy leak. We propose a novel extremely scalable hybrid LSH-CBSAA method to anonymize big dataset to provide data privacy. There are two phases in our proposed method. First phase divides the given original dataset into smaller units using LSH. Min-Hash function is engaged to split datasets into several divisions to parallelize computation. In the second phase, CBSAA (Constraint Based Sensitive Attribute Anonymization) is …