Data Anonymization Approaches for Data Sets Using Map Reduce on Cloud: A Survey

In this information age, huge amounts of data are collected and mined every day. The process of data publication is becoming larger and complex day by day. Cloud computing is the most popular model for supporting large and complex data, most organizations are moving towards to reduce their cost and elasticity features. However cloud computing has potential risk and vulnerabilities. One of major problem in moving to cloud computing is its security and privacy concerns. Cloud computing provides powerful and economical infrastructural resources for cloud users to handle ever increasing data sets in big data applications. However, processing or sharing privacy-sensitive data sets on cloud probably leads to privacy concerns because of multi-tenancy system. Data encryption and anonymization is two widely-adopted ways to combat privacy breach. The encryption is not suitable for data that are processed and shared frequently, and the anonymizing big data and manage anonymized data sets are still challenges for traditional anonymization approaches. Thus, variousproposals have been designed in a cloud computing for privacy preserving in datapublishing. In this paper, we survey the current existingtechniques, and analyze the advantage and disadvantage ofthese approaches.