Adapting MapReduce for Efficient Watermarking of Large Relational Dataset

Rani, Sapana; Koshley, Dileep Kumar; Halder, Raju

doi:10.1109/trustcom/bigdatase/icess.2017.306

Cited by 5 publications

(2 citation statements)

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“…These fake tuples are stored in a separate file, not inside the database, therefore making this approach distortion-free. Authors in [72] have adapted the MapReduce paradigm for watermarking of relational databases to decrease the computational cost and have implemented distortion-free algorithms in both sequential and MapReduce form. The proposal in [73] generates an image as a watermark from the database content.…”

Section: E: Othersmentioning

confidence: 99%

“…2) Watermark generation and detection time is least in case of [70] and highest in case of [64]. Authors in [72] adapted the MapReduce paradigm to watermark relational databases. They have implemented the algorithms proposed in [57], [64], [67], [69], [70] in sequential as well as MapReduce form and it was observed that as the data size increases, the percentage reduction in watermarking time increases from sequential to MapReduce.…”

Section: A Computational Timementioning

confidence: 99%

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Comparative Analysis of Relational Database Watermarking Techniques: An Empirical Study

Rani

Halder

2022

IEEE Access

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Digital watermarking is considered one of the most promising techniques to verify the authenticity and integrity of digital data. It is used for a wide range of applications, e.g., copyright protection, tamper detection, traitor tracing, maintaining the integrity of data, etc. In the past two decades, a wide range of algorithms for relational database watermarking has been proposed. Even though a number of surveys exist in the literature, they are unable to provide insightful guidance to choose the right watermarking technique for a given application. In this paper, we provide an exhaustive empirical study and thorough comparative analysis of various relational database watermarking techniques in the literature. Our work is different from the existing survey papers as we consider both distortion-based and distortion-free techniques along with a rigorous experimental analysis demonstrating a detailed comparison on robustness, data usability, and computational cost with considerable empirical evidence. INDEX TERMSDigital watermarking, relational database, empirical study, robustness, data usability. RAJU HALDER (Member, IEEE) received the Ph.D. degree from Università Ca´Foscari, Italy, in 2012. He is currently an Associate Professor with the Department of Computer Science and Engineering, IIT Patna. Before joining IIT Patna, he worked as a Postdoctoral Researcher with Macquarie University, Australia. He worked with the Robotics Team at HASLab (University of Minho), Portugal, in 2016. Prior to his Ph.D. degree, he had also worked as an Associate System Engineer at IBM India Private Ltd., from 2007 to 2008. His research interests include formal methods, blockchain technology, program analysis and verification, and data privacy and security.

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Section: E: Othersmentioning

confidence: 99%