Data Anonymization Approach For Privacy Preserving In Cloud

Private data such as electronic health records and banking transactions must be shared within the cloud environment to analysis or mine data for research purposes. Data privacy is one of the most concerned issues in big data applications, because processing large-scale sensitive data sets often requires computation power provided by public cloud services. A technique called Data Anonymization, the privacy of an individual can be preserved while aggregate information is shared for mining purposes. Data Anonymization is a concept of hiding sensitive data items of the data owner. A bottom-up generalization approach for transforming more specific data to less specific but semantically consistent data in order to preserve privacy. The idea is to explore the data generalization from data mining to hide detailed data, rather than discovering the patterns. When the data is masked, data mining techniques can be applied without modification. Keywords—Data Anonymization; Cloud; Bottom Up Generalization; Mapreduce; Privacy Preservation.