26-12-2009, 07:45 PM
Watermarking Relational Databases using Optimization Based Techniques
Watermarking techniques have emerged as animportant building
block which plays a crucial role in addressing the ownership
problemIn this article we describe a mechanism for proof of
ownership based on the secure embedding of a robust imperceptible
watermark in relational data.Earlier, Proving ownership rights
on outsourced relational databases was a critical issue in
internet-based application environments and in many content
distribution applications. This watermarking technique is
immune to watermark synchronization errors because it uses a
partitioning approach that does not require marker tuples. This
technique overcomes a major drawback of previous methods in that
Watermark decoding is based on a threshold-based technique
characterized by an optimal threshold that minimizes the
probability of decoding errors. The watermarking of relational
databases is done as a constrained optimization problem, and
efficient techniques to solve the optimization problem and to
handle the constraints are discussed. Experimental results
show that this technique is resilient to alteration, tuple
deletion, and insertion attacks.
Watermarking techniques
A watermark describes information that can be used to prove
the ownership of data. Imperceptible embedding means that the
presence of the watermark is unnoticeable in the data.
Also, the watermark detection is blinded ,that is, it neither
requires the knowledge of the original data nor the watermark.
The approach
A data set D is transformed into a watermarked version DW by
applying a watermark encoding function that also takes as
inputs a secretkey Ks only known to the copyright owner
and a watermark W.Data modifications by watermark
are controlled by providing usability constraints referred to by
the set G.The watermark encoding has the following three steps:
1)Data set partitioning: the data set D is partitioned into m non-overlapping partitions by using key Ks.
2)Watermark embedding: a watermark bit is embedded in
each partition by altering the partition statistics while still verifying
the usability constraints in G.
3)Optimal threshold evaluation: the bit embedding statistics
are used to compute the optimal threshold that minimizes
the probability of decoding error.
Watermark decoding: It is the process of extracting the embedded
watermark using the watermarked data set DW, the secret key Ks
and the optimal threshold. It consists of the following steps:
1)Data set partitioning where the data partitions are generated
2)Threshold based decoding: the statistics of each partition
are evaluated and the embedded bit is decoded using a threshold
based scheme based on the optimal threshold.
3)Majority voting: The watermark bits are decoded using
a majority voting technique.
Genetic algorithm and pattern search techniques are employed to solve
the optimization problem and to handle the constraints.
Seminar report download:
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