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Publicly verifiable ownership protection for relational databases
Author(s) -
Yingjiu Li,
Robert H. Deng
Publication year - 2006
Publication title -
singapore management university institutional knowledge (ink) (singapore management university)
Language(s) - English
Resource type - Conference proceedings
ISBN - 1-59593-272-0
DOI - 10.1145/1128817.1128832
Subject(s) - digital watermarking , computer science , watermark , computer security , relational database , key (lock) , verifiable secret sharing , context (archaeology) , public key cryptography , distortion (music) , scheme (mathematics) , database , categorical variable , theoretical computer science , image (mathematics) , encryption , mathematics , computer network , artificial intelligence , paleontology , mathematical analysis , set (abstract data type) , bandwidth (computing) , machine learning , biology , programming language , amplifier
Today, watermarking techniques have been extended from the multimedia context to relational databases so as to protect the ownership of data even after the data are published or distributed. However, all existing watermarking schemes for relational databases are secret key based, thus require a secret key to be presented in proof of ownership. This means that the ownership can only be proven once to the public (e.g., to the court). After that, the secret key is known to the public and the embedded watermark can be easily destroyed by malicious users. Moreover, most of the existing techniques introduce distortions to the underlying data in the watermarking process, either by modifying least significant bits or exchanging categorical values. The distortions inevitably reduce the value of the data. In this paper, we propose a watermarking scheme by which the ownership of data can be publicly proven by anyone, as many times as necessary. The proposed scheme is distortion-free, thus suitable for watermarking any type of data without fear of error constraints. The proposed scheme is robust against typical database attacks including tuple/attribute insertion/deletion, random/selective value modification, data frame-up, and additive attacks

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