An efficient format-independent watermarking framework for large-scale data sets

作者：

Highlights：

• Large-scale data sets watermarking.

• Adapted MapReduce, Pig and Hive paradigms.

• Designed a middleware that supports Format-independent watermarking.

• CSV, JSON and XML data formats are considered.

摘要

•Large-scale data sets watermarking.•Adapted MapReduce, Pig and Hive paradigms.•Designed a middleware that supports Format-independent watermarking.•CSV, JSON and XML data formats are considered.

论文关键词：Digital watermarking,Large-scale data sets,MapReduce,Pig,Hive

论文评审过程：Received 16 May 2021, Revised 17 June 2022, Accepted 4 July 2022, Available online 8 July 2022, Version of Record 15 July 2022.

论文官网地址：https://doi.org/10.1016/j.eswa.2022.118085