Query-preserving watermarking of relational databases and XML documents
Proceedings of the twenty-second ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Rights protection for relational data
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Constructing a virtual primary key for fingerprinting relational data
Proceedings of the 3rd ACM workshop on Digital rights management
Watermarking relational data: framework, algorithms and analysis
The VLDB Journal — The International Journal on Very Large Data Bases
Tamper detection and localization for categorical data using fragile watermarks
Proceedings of the 4th ACM workshop on Digital rights management
Rights Protection for Categorical Data
IEEE Transactions on Knowledge and Data Engineering
Rights assessment for discrete digital data
Rights assessment for discrete digital data
Effective approaches for watermarking XML data
DASFAA'05 Proceedings of the 10th international conference on Database Systems for Advanced Applications
Reversible and blind database watermarking using difference expansion
Proceedings of the 1st international conference on Forensic applications and techniques in telecommunications, information, and multimedia and workshop
Digital watermarking for relational databases using traceability parameter
International Journal of Computer Applications in Technology
Subset selection approach for watermarking relational databases
ICDEM'10 Proceedings of the Second international conference on Data Engineering and Management
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In this paper, we propose a fingerprinting solution to protect valuable numeric relational data from illegal duplications and redistributions. We introduce a twice-embedding scheme. In the first embedding process, we embed a unique fingerprint to identify each recipient to whom the relational data is distributed. The embedding process is controlled by a secret key. Meanwhile, the fingerprint can be detected using the same secret key to prove ownership at a numerical confidence level. The second embedding process is designed for verifying the extracted fingerprint and giving a numerical confidence level. Thus, once a suspect copy is found, numerical confidence level can be provided both to identify the owner and the illegal distributor. The experiment shows that our solution is effective and robust to various attacks.