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Tamper detection and localization for categorical data using fragile watermarks
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Source ACM Workshop On Digital Rights Management archive
Proceedings of the 4th ACM workshop on Digital rights management table of contents
Washington DC, USA
SESSION: Marking and tracing methods table of contents
Pages: 73 - 82  
Year of Publication: 2004
ISBN:1-58113-969-1
Authors
Yingjiu Li  Singapore Management University, Singapore
Huiping Guo  George Mason University, Fairfax, VA
Sushil Jajodia  George Mason University, Fairfax, VA
Sponsors
SIGSAC: ACM Special Interest Group on Security, Audit, and Control
ACM: Association for Computing Machinery
Publisher
ACM  New York, NY, USA
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Downloads (6 Weeks): 3,   Downloads (12 Months): 76,   Citation Count: 5
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ABSTRACT

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.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
1
R. Agrawal and J. Kiernan. Watermark relational databases. In Proc. of the 28th Inter. Conf. On Very Large Data Bases, 2002.
 
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M. Chen, Y. He, and R. Lagendijk. A fragile watermark error detection scheme for wireless video communications. IEEE Trans. On Multimedia, pages 315--329, August 2003.
 
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J. Fridrich and M. Du. Images with self-correcting capabilities. In Proc. of the IEEE Inter. Conf. On Image Processing, pages 792--796, 1999.
 
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J. Fridrich, M. Goljan, and M. Du. Invertible authentication. In Proc. Of SPIE, Security and Watermarking of Multimedia Contents, January 2001.
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Y. Li, V. Swarup, and S. Jajodia. A robust watermarking scheme for relational data. In Proc. The 13th workshop on information technology and engineering, pages 195--200, December 2003.
 
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E. Lin and E. Delp. A review of fragile image watermarks. In Proc. Of the Multimedia and Security Workshop (ACM Multimedia '99), October 30 - November 5, 1999.
 
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E. Mykletun, M. Narasimha, and G. Tsudik. Authentication and integrity in outsourced databases. In Proc. Of the Network and Distributed System Security Symposium 2004), Feb 2004.
 
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Collaborative Colleagues:
Yingjiu Li: colleagues
Huiping Guo: colleagues
Sushil Jajodia: colleagues