Recently, a significant amount of interest has been developed in motivating physicians to use e-health technology (especially Electronic Medical Records (EMR) systems). An important utility of such EMR systems is: a next generation of Clinical Decision Support Systems (CDSS) will extract knowledge from these electronic medical records to enable physicians to do accurate and effective diagnosis. It is anticipated that in future such medical records will be shared through cloud among different physicians to improve the quality of health care. Therefore, right protection of medical records is important to protect their ownership once they are shared with third parties. Watermarking is a proven well known technique to achieve this objective. The challenges associated with watermarking of EMR systems are: (1) some fields in EMR are more relevant in the diagnosis process; as a result, small variations in them could change the diagnosis, and (2) a misdiagnosis might not only result in a life threatening scenario but also might lead to significant costs of the treatment for the patients. The major contribution of this paper is an information-preserving watermarking scheme to address the above-mentioned challenges. We model the watermarking process as a constrained optimization problem. We demonstrate, through experiments, that our scheme not only preserves the diagnosis accuracy but is also resilient to well known attacks for corrupting the watermark. Last but not least, we also compare our scheme with a well known threshold-based scheme to evaluate relative merits of a classifier. Our pilot studies reveal that -- using proposed information-preserving scheme -- the overall classification accuracy is never degraded by more than 1 . In comparison, the diagnosis accuracy, using the threshold-based technique, is degraded by more than 18 in a worst case scenario.
@cite_9 presented a watermarking scheme in which data is partitioned using marker tuples. But if an attacker launches an attack on marker tuples, the synchronization is lost. The marker tuples decide the boundaries for the partitions but successful insertion or deletion attacks on marker tuples would result in synchronization errors. @cite_24 have proposed a watermarking technique to detect and localize alterations made to a database relation with relevant attributes. have proposed a technique for fingerprinting relational data @cite_25 by extending the technique proposed in @cite_6 . proposed an invisible watermarking technique using Discrete Wavelet Transform (DWT) in @cite_2 .
@cite_9 @cite_6 @cite_24 @cite_2 @cite_25
2100913369 2039690308 2036999337 2105602218 2145419924
we introduce a solution for relational database content rights protection through watermarking. Rights protection for relational data is of ever-increasing interest, especially considering areas where sensitive, valuable content is to be outsourced. A good example is a data mining application, where data is sold in pieces to parties specialized in mining it. Different avenues are available, each with its own advantages and drawbacks. Enforcement by legal means is usually ineffective in preventing theft of copyrighted works, unless augmented by a digital counterpart, for example, watermarking. While being able to handle higher level semantic constraints, such as classification preservation, our solution also addresses important attacks, such as subset selection and random and linear data changes. We introduce wmdb., a proof-of-concept implementation and its application to real-life data, namely, in watermarking the outsourced Wal-Mart sales data that we have available at our institute. Abstract.We enunciate the need for watermarking database relations to deter data piracy, identify the characteristics of relational data that pose unique challenges for watermarking, and delineate desirable properties of a watermarking system for relational data. We then present an effective watermarking technique geared for relational data. This technique ensures that some bit positions of some of the attributes of some of the tuples contain specific values. The specific bit locations and values are algorithmically determined under the control of a secret key known only to the owner of the data. This bit pattern constitutes the watermark. Only if one has access to the secret key can the watermark be detected with high probability. Detecting the watermark requires access neither to the original data nor the watermark, and the watermark can be easily and efficiently maintained in the presence of insertions, updates, and deletions. Our analysis shows that the proposed technique is robust against various forms of malicious attacks as well as benign updates to the data. Using an implementation running on DB2, we also show that the algorithms perform well enough to be used in real-world applications. Today, database relations are widely used and distributed over the Internet. Since these data can be easily tampered with, it is critical to ensure the integrity of these data. In this paper, we propose to make use of fragile watermarks to detect and localize malicious alterations made to a database relation with categorical attributes. Unlike other watermarking schemes which inevitably introduce distortions to the cover data, the proposed scheme is distortion free. In our algorithm, all tuples in a database relation are first securely divided into groups according to some secure parameters. Watermarks are embedded and verified in each group independently. Thus, any modifications can be localized to some specific groups. Theoretical analysis shows that the probability of missing detection is very low. This paper primarily researches on the feasibility of embedding watermark to relational databases in the Discrete Wavelet Transform (DWT) domain. Watermark embedding is known to be difficult for relational databases. This paper focuses on the analysis of the wavelet high frequency coefficients of corresponding data and gives the definition of the intensive factor. By employing the linear correlation detecting method, this paper proposes the watermarking algorithm, which can embed the watermark into relational database successfully in DWT domain. The watermark can be distributed to different parts of the relational database. Experiments show that the embedded digital watermarks in the proposed algorithm are invisible and some degree of the robustness of the proposed algorithm against the commonly attacks that are used in databases processing. In this paper, we present a technique for fingerprinting relational data by extending 's watermarking scheme. The primary new capability provided by our scheme is that, under reasonable assumptions, it can embed and detect arbitrary bit-string marks in relations. This capability, which is not provided by prior techniques, permits our scheme to be used as a fingerprinting scheme. We then present quantitative models of the robustness properties of our scheme. These models demonstrate that fingerprints embedded by our scheme are detectable and robust against a wide variety of attacks including collusion attacks.