Privacy Preserving Parallel Clustering Based Anonymization for Big Data Using MapReduce Framework

Big data refers to a massive volume of collected from heterogeneous sources including Internet Things (IoT) devices. analytics is playing crucial role in extracting patterns that would benefit efficient and effective decision making. Processing this poses several critical issues such as scalability, security privacy. To preserve privacy, numerous privacy-preserving mining publishing techniques exist. Data anonymization utilizing for preserving an individual’s privacy promising approach prevent the against identity disclosure. In paper, Parallel Clustering based Anonymization Algorithm (PCAA) proposed, results prove algorithm scalable also achieves better tradeoff between utility. The MapReduce framework used parallelize process handling huge data. performs well terms classification accuracy, F-measure, Kullback–Leibler divergence metrics. Moreover, big generated are efficiently protected meet ever-growing requirements application.