Databases Reference
In-Depth Information
Ultimately, we believe the process of resilient information hiding will become
available as a secure mechanism for not only rights protection but also data
tracing and authentication in a multitude of discrete data frameworks.
7 Conclusions
In this chapter we explored how Information Hiding can be successfully de-
ployed as a tool for Rights Assessment for discrete digital Works. We analyzed
solutions for resilient Information Hiding for relational data, including numeric
and categorical types.
A multitude of associated future research avenues present themselves in a
relational framework, including: the design of alternative primary or pseudo-
primary key independent encoding methods, a deeper theoretical understand-
ing of limits of watermarking for a broader class of algorithms, the ability to
better defeat additive watermark attacks, an exploration of zero-knowledge
watermarking etc.
Moreover, while the concept of on-the-fly quality assessment for a consumer-
driven design has the potential to function well, another interesting avenue
for further research would be to augment the encoding method with direct
awareness of semantic consistency (e.g., classification and association rules).
This would likely result in an increase in available encoding bandwidth, thus
in a higher encoding resilience. One idea would be to define a generic language
(possibly subset of SQL) able to naturally express such constraints and their
propagation at embedding time.
Additionally, of particular interest for future research exploration, we en-
vision cross-domain applications of Information Hiding in distributed envi-
ronments such as sensor networks, with applications ranging from resilient
content annotation to runtime authentication and data integrity proofs.
8 Acknowledgments
The author is supported partly by the NSF through awards CT CNS-0627554,
CT CNS-0716608 and CRI CNS 0708025. The author also wishes to thank
Motorola Labs, IBM Research, CEWIT, and the Stony Brook Oce of the
Vice President for Research.
References
1. Rakesh Agrawal, Peter J. Haas, and Jerry Kiernan. Watermarking relational
data: framework, algorithms and analysis.
The VLDB Journal
, 12(2):157-169,
2003.
