{"title":"Reversible Database Watermarking Based on Order-preserving Encryption for Data Sharing","authors":"Donghui Hu, Qing Wang, Song Yan, Xiaojun Liu, Meng Li, Shuli Zheng","doi":"10.1145/3589761","DOIUrl":null,"url":null,"abstract":"In the era of big data, data sharing not only boosts the economy of the world but also brings about problems of privacy disclosure and copyright infringement. The collected data may contain users’ sensitive information; thus, privacy protection should be applied to the data prior to them being shared. Moreover, the shared data may be re-shared to third parties without the consent or awareness of the original data providers. Therefore, there is an urgent need for copyright tracking. There are few works satisfying the requirements of both privacy protection and copyright tracking. The main challenge is how to protect the shared data and realize copyright tracking while not undermining the utility of the data. In this article, we propose a novel solution of a reversible database watermarking scheme based on order-preserving encryption. First, we encrypt the data using order-preserving encryption and adjust an encryption parameter within an appropriate interval to generate a ciphertext with redundant space. Then, we leverage the redundant space to embed robust reversible watermarking. We adopt grouping and K-means to improve the embedding capacity and the robustness of the watermark. Formal theoretical analysis proves that the proposed scheme guarantees correctness and security. Results of extensive experiments show that OPEW has 100% data utility, and the robustness and efficiency of OPEW are better than existing works.","PeriodicalId":50915,"journal":{"name":"ACM Transactions on Database Systems","volume":"48 1","pages":"1 - 25"},"PeriodicalIF":2.2000,"publicationDate":"2023-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACM Transactions on Database Systems","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1145/3589761","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
引用次数: 2
Abstract
In the era of big data, data sharing not only boosts the economy of the world but also brings about problems of privacy disclosure and copyright infringement. The collected data may contain users’ sensitive information; thus, privacy protection should be applied to the data prior to them being shared. Moreover, the shared data may be re-shared to third parties without the consent or awareness of the original data providers. Therefore, there is an urgent need for copyright tracking. There are few works satisfying the requirements of both privacy protection and copyright tracking. The main challenge is how to protect the shared data and realize copyright tracking while not undermining the utility of the data. In this article, we propose a novel solution of a reversible database watermarking scheme based on order-preserving encryption. First, we encrypt the data using order-preserving encryption and adjust an encryption parameter within an appropriate interval to generate a ciphertext with redundant space. Then, we leverage the redundant space to embed robust reversible watermarking. We adopt grouping and K-means to improve the embedding capacity and the robustness of the watermark. Formal theoretical analysis proves that the proposed scheme guarantees correctness and security. Results of extensive experiments show that OPEW has 100% data utility, and the robustness and efficiency of OPEW are better than existing works.
期刊介绍:
Heavily used in both academic and corporate R&D settings, ACM Transactions on Database Systems (TODS) is a key publication for computer scientists working in data abstraction, data modeling, and designing data management systems. Topics include storage and retrieval, transaction management, distributed and federated databases, semantics of data, intelligent databases, and operations and algorithms relating to these areas. In this rapidly changing field, TODS provides insights into the thoughts of the best minds in database R&D.