With the emergence of distributed programming frameworks like Hadoop and cloud computing technology, big data and its analytics became a reality. As big data needs huge amount of storage and computing resources, cloud has given solution to the needs of big data. However, it is important to protect big data from privacy attacks. Disclosure of identity of an entity or organization or a person in the big data is an example for loss of privacy. In other words, non-disclosure of privacy of certain sensitive attributes is nothing but preserving privacy of big data. As traditional computing is replaced by Internet based computing, it became essential to deal with privacy of big data. Many techniques came into existence to protect big data. In this paper, we considered a specific case where an adversary launches attack to know the presence or absence of an entity in the big data. We proposed an algorithm based on differential privacy to withstand the aforementioned privacy attack on big data workload in MapReduce programming paradigm. We built a prototype application and deployed it in Elastic MapReduce (EMR) of Amazon Elastic Compute Cloud (EC2). The experimental results revealed the utility of the proposed algorithm and showed proof of the concept.

Big data, big data privacy, differential privacy, Elastic MapReduce (EMR)

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