Double Privacy Layer Architecture for Big Data Framework

Big data is an emerging and very considerable technology for gathering and analyzing a huge volume of real-time produced data efficiently and effectively, but it has also a great volume of sensitive data arising invasion of privacy. Big data analyses can give us very customized and effective analysis results, but this technology can be abused for privacy invasion of personal users. This paper introduces sensitive information which can be collected and/or synthesized at the data collection stage, data analysis stage, and presentation service stage. And then this paper proposes a double layered architecture for preserving user privacy. The proposed architecture provides two steps for masking sensitive information from the big data processing databases, and these steps are presented in detail with some examples in this paper.