Adapting MapReduce for Efficient Watermarking of Large Relational Dataset

In the era of big-data when volume is increasing at an unprecedented rate, structured data is not an exception from this. A survey in 2013 by TDWI says that, for a quarter of organizations, big-data mostly takes the form of the relational and structured data that comes from traditional applications. In this reality, watermarking of large volume of structured relational dataset using existing watermarking techniques are highly inefficient, and even impractical in the situations when periodic rewatermarking after a certain time frame is necessary. As a remedy of this, in this paper, we adapt MapReduce as an effective distributive way of watermarking of large relational dataset. We show how existing algorithms can easily be converted into an equivalent form in MapReduce paradigm. We present experimental evaluation results on a benchmark dataset to establish the effectiveness of our approach. The results demonstrate significant improvements in watermark generation and detection times w.r.t. existing works in the literature.