A k-Anonymity-Based Robust Watermarking Scheme for Relational Database

Jing Yu, Shuguang Yuan, Yulin Yuan, Yafan Li, Chi Chen

Abstract: In the era of big data, secure and controlled data publishing becomes increasingly vital. When data holders publish dataset to data demanders, data holders often (1) protect the copyright of the published dataset and (2) anonymize user’s data by k-anonymity for privacy purpose. Hence, there is a realistic demand of watermarked k-anonymity dataset for ownership. However, there are two important challenges to be addressed: the lack of primary key and the narrow bandwidth channel for watermarked k-anonymity dataset. In this paper, we try to address above challenges by proposing a k-anonymity-based robust watermarking scheme in anonymized dataset by an “one-time” way to achieve both protection of privacy and copyright. This scheme is primary key independent and meets the requirement of keeping the same form with k-anonymity. Experimental studies prove the robustness of watermarking scheme against subset deletion and subset addition attacks.