Main Article Content
Data security and privacy are essential things nowadays due to a large amount of sensitive data broadcasting on social media websites. Most of Internet of Things (IoT) and health care applications having quite challenges to achieve privacy preservation when number of distributed resources has involved during the data broadcasting. In this paper, we proposed a distributed data analysis and privacy preservation framework. In this paper, we introduced numerous privacy preservation techniques during data distribution to achieve high privacy. Some traditional methods, data anonymization, generalization, random permutation, specialization, top-down and bottom-up data generalization, fingerprint insertion etc., are also evaluated on extensive data when distributing with multi parties: the proposed one-way hashing privacy, XOR operation for generating multiple secure copies. First, we develop a few dynamic policies for each copy using XOR operation and insert fingerprints in individual documents. The collaboration of XOR operation and custom policies archives higher security from internal as well as external attacks. On the other hand, the data recovery approach has been designed to extract a fingerprint from secure copies. In the extensive experimental analysis, we evaluate proposed results with numerous existing systems and show the effectiveness of proposed modules in a distributed environment.