WATERMARKING RELATIONAL DATABASES USING OPTIMIZATION-BASED TECHNIQUES
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13-01-2010, 07:37 AM


WATERMARKING RELATIONAL DATABASES USING OPTIMIZATION-BASED TECHNIQUES

abstract:
Proving ownerships rights on outsourced relational database is a crucial issue in today's internet based application environments and in many content distribution applications. In this paper, we present a mechanism for proof of ownership based on the secure embedding of a robust imperceptible watermark in relational data. We formulate the watermarking of relational databases as a constrained optimization problem and discus efficient techniques to solve the optimization problem and to handle the onstraints. Our watermarking technique is resilient to watermark synchronization errors because it uses a partioning approach that does not require marker tuple. Our approach overcomes a major weakness in previously proposed watermarking techniques. Watermark decoding is based on a threshold-based technique characterized by an optimal threshold that minimizes the probability of decoding errors. We implemented a proof of concept implementation of our watermarking technique and showed by experimental results that our technique is resilient to tuple deletion, alteration and insertion attacks
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17-04-2010, 09:06 PM

i want ful documentation of the project and implimentation at any cost
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24-06-2010, 11:15 PM

to get full documentation of the idea WATERMARKING RELATIONAL DATABASES USING OPTIMIZATION-BASED TECHNIQUES please see citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.114.6299&rep=rep1&type=pdf or portal.acmcitation.cfm?id=1340185
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04-10-2010, 01:20 PM


.doc   Watermarking Relational Databases using Optimization.doc (Size: 532 KB / Downloads: 213)
Watermarking Relational Databases using Optimization
ABSTRACT
Proving ownership rights on outsourced relational databases is a crucial issue in today's internet-based application environments and in many content distribution applications. In this paper, we present a mechanism for proof of ownership based on the secure embedding of a robust imperceptible watermark in relational data. We formulate the watermarking of relational databases as a constrained optimization problem and discuss efficient techniques to solve the optimization problem and to handle the constraints. Our watermarking technique is resilient to watermark synchronization errors because it uses a partitioning approach that does not require marker tuples. Our approach overcomes a major weakness in previously proposed watermarking techniques. Watermark decoding is based on a threshold-based technique characterized by an optimal threshold that minimizes the probability of decoding errors. We implemented a proof of concept implementation of our watermarking technique and showed by experimental results that our technique is resilient to tuple deletion, alteration, and insertion attacks.

INTRODUCTION


The rapid growth of internet and related technologies has offered an unprecedented ability to access and redistribute digital contents. In such a context, enforcing data ownership is an important requirement which requires articulated solutions, encompassing technical, organizational and legal aspects. Though we are still far from such comprehensive solutions, in the last years watermarking techniques have emerged as an important building block which plays a crucial role in addressing the ownership problem. Such techniques allow the owner of the data to embed an imperceptible watermark into the data.

A watermark describes information that can be used to prove the ownership of data, such as the owner, origin, or recipient of the content. Secure embedding requires that the embedded watermark must not be easily tampered with, forged, or removed from the watermarked data. Imperceptible embedding means that the presence of the watermark is unnoticeable in the data. Furthermore, the watermark detection is blinded, that is, it neither requires the knowledge of neither the original data nor the watermark. Watermarking techniques have been developed for video, images, audio, and text data and also for software and natural language text.

By contrast the problem of watermarking relational data has not been given appropriate attention. There are, however, many application contexts for which data represent an important asset, the ownership of which must thus be carefully enforced. This is the case, for example, of weather data, stock market data, power consumption, consumer behavior data, medical and scientific data.



Watermark embedding for relational data is made possible by the fact that real data can very often tolerate a small amount of error without any significant degradation with respect to their usability. For example when dealing with weather data, changing some daily temperatures of 1 or 2 degrees is a modification that leaves the data still usable.

To date only a few approaches to the problem of watermarking relational data have been proposed. These techniques, however, are not very resilient to watermark attacks. In this project and implimentation, we present a watermarking technique for relational data that is highly resilient compared to these techniques. In particular, our proposed technique is resilient to tuple deletion, alteration, and insertion attacks.
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#5
13-10-2010, 11:38 AM



.pdf   agarwal algorithm.pdf (Size: 203.67 KB / Downloads: 134)
Watermarking Relational Databases

Rakesh Agrawal Jerry Kiernan
IBM Almaden Research Center
650 Harry Road, San Jose, CA 95120

Abstract
We enunciate the need for watermarking database relations to deter their piracy, identify the unique characteristics of relational data which pose new challenges for watermarking, and provide desirable properties of a watermarking system for relational data. A watermark can be applied to any database relation having attributes which are such that changes in a few of their values do not affect the applications. 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 tuples, attributes within a tuple, bit positions in an attribute, and specific bit values are all algorithmically determined under the control of a private key known only to the owner of the data. This bit pattern constitutes the watermark. Only if one has access to the private key can the watermark be detected with high probability. Detecting the watermark neither requires access to the original data nor the watermark. The watermark can be detected even in a small subset of a watermarked relation as long as the sample contains some of the marks. Our extensive analysis shows that the proposed technique is robust against various forms of malicious attacks and updates to the data. Using an implementation running on DB2, we also show that the performance of the algorithms allows for their use in real world applications.
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05-03-2011, 02:29 PM


.pptx   final pre.pptx (Size: 2.09 MB / Downloads: 105)
Introduction to watermarking
 In today’s internet environment proving ownership rights on outsourced relational databases is a crucial issue.
 For that purpose we present the mechanism for proof of
Ownership based on secure embedding of robust imperceptible watermark in relational data
 Here we formulate the watermarking relational databases as a constrained optimization problem and discuss efficient techniques to solve optimization problem to handle the constraints. our technique is resilient to watermark synchronization errors because it uses the partition approach that does not require marker tuples
 Watermarking is a technique that allow the owner of the data to embed an imperceptible watermark into the data.
Aim and Objective
Aim:-
The main Aim of Watermarking Relational Databases is develop a mechanism for proof of ownership rights on outsourced relational databases.
Objective:-
Develop a secure, robust and imperceptible watermark
mechanism for providing ownership rights.
 Watermarking Model
 Applications of watermarking
 Copyright Protection
 Data Hiding
 Authentication and Data Integrity
 Copy Protection
 Fingerprinting
 Why Watermarking ?
 Digital Media (Video, Audio, Images, Text) are easily copied and easily distributed via the web.
 Database outsourcing is a common practice:
◦ Stock market data
◦ Consumer Behavior data (Walmart)
◦ Power Consumption data
◦ Weather data
 Effective means for proof of authorship.
◦ Signature and data are the same object.
 Effective means of tamper proofing.
◦ Integrity information is embedded in the data.
 What Makes Watermarking Databases Different?
 Dealing with multiple objects (tuples) instead of one
 Tuple order does not matter
 After dropping part of the database, the remaining part is still valuable
 Simple Example for Genetic Algorithms
NP Complete problems
Problems in which it is very difficult to find solution, but once we have it, it is easy to check the solution.

Nobody knows if some faster algorithm exists to provide exact answers to NP-problems. An example of alternate method is the genetic algorithm.
Example: Traveling salesman problem.
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24-03-2011, 03:54 PM


.pptx   Watermarking Relational Databases Using Optimization- Based Techniques.pptx (Size: 280.8 KB / Downloads: 58)
Watermarking Relational Databases Using Optimization- Based Techniques
Abstraction

 Proving ownership rights on outsourced relational databases
 We present a mechanism for proof of ownership based on secure embedding of robust(Sturdy in construction)
 Here we formulate the constrains and discuss efficient tech to solve the optimization prob and to handle the constrains
 Overcomes a major weakness in previously proposed tech
 Probability of decoding errors are minimized by an optimal threshold(Threshold-Based Tech)
Introduction
 Enforcing data ownership is an important requirement which requires articulated solutions
 In last years watermarking techniques have emerged as an important building block which plays a crucial role in addressing the ownership problem.
 A watermark describes information that can be used to prove the ownership of data, such as the owner, origin, or recipient of the content.
 Watermarking techniques have been developed for video, images, audio, and text data and also for software and natural language text
 application contexts for which data represent an important asset, the ownership of which must thus be carefully enforced.
 For example, of weather data, stock market data, power consumption, consumer behavior data, medical and scientific data
Watermarking
Main Contribution

 We formulate the watermarking of relational databases as a constrained optimization problem, and discuss efficient techniques to handle the constraints. We present two techniques to solve the formulated optimization problem based on genetic algorithms and pattern search techniques.
 We present a data partitioning technique that does not depend on marker tuples to locate the partitions and thus it is resilient to watermark synchronization errors.
 We develop an efficient technique for watermark detection that is based on an optimal threshold. The optimal threshold is selected by minimizing the probability of decoding error.
 With a proof of concept implementation of our watermarking technique, we have conducted experiments using both synthetic and real-world data. We have compared our watermarking technique with previous approaches shows the superiority of our technique with respect to all types of attacks.
Related Work
 1. Agrawal proposed a watermarking algorithm that embeds the watermark bits in the least significant bits (LSB) of selected attributes of a selected subset of tuples
 This technique does not provide a mechanism for multi bit watermarks; instead only a secret key is used.
 For each tuple, a secure message authenticated code (MAC) is computed using the secret key and the tuple’s primary key.
Hiding bits in LSB is efficient.
 Problem : the watermark can be easily compromised by very trivial attacks.
 2. Sion et al proposed a watermarking technique that embeds watermark bits in the data statistics.
 data partitioning technique used is based on the use of special marker tuples which makes it vulnerable to watermark synchronization errors resulting from tuple deletion and tuple insertion; thus such technique is
not resilient to deletion and insertion attacks.
 He recommend storing the marker tuples to enable the decoder to accurately reconstruct the underlying partitions
 Problem: violates the blinded watermark detection property
 Furthermore, Sion et al. proposed
 a threshold technique for bit decoding that is based on two thresholds.
 However, the thresholds are arbitrarily chosen without any optimality criteria.
 Thus the decoding algorithm exhibits errors resulting from the non-optimal threshold selection
 Even in the absence of an attacker.
Next Comes Gross-Amblard
 Gross-Amblard proposed a watermarking technique for
XML documents and theoretically investigates links between query result preservation and acceptable watermarking alterations.
 Another interesting related research effort is to be found in where the authors have proposed a fragile watermark technique
 to detect and localize alterations made to a database relation with categorical attributes.
APPROACH OVERVIEW
 The Main Components are as follows
 Data set “D”
 Data set “D” is transformed into a watermarked version “DW”
 watermark encoding function that also takes as inputs a secret key “Ks”
 only known to the copyright owner and a watermark “W”
 Watermarking modifies the data.
 modifications
 are controlled by providing usability constraints referred to by the set G.
Watermarking Encoding
summarized by the following three steps:
 Step E1: Data set partitioning: by using the secret key Ks
the data set D is partitioned into m non-overlapping partitions {S0, . . . , Sm−1}.
 Step E2: Watermark embedding: a watermark bit is embedded in each partition by altering the partition statistics while still verifying the usability constraints in G. This alteration is performed by solving a constrained optimization problem.
 Step E3: Optimal threshold evaluation: the bit embedding statistics are used to compute the optimal threshold T∗ that minimizes the probability of decoding error.
Watermark Decoding
 The watermark decoding is divided into three main steps:
 Step D1: Data set partitioning: by using the data partitioning
algorithm used in E1, the data partitions are generated.
 Step D2: 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 T∗.
 Step D3: Majority voting: The watermark bits are decoded using a majority voting technique.
Watermark Encoding Algorithms
 Data Partitioning
 Watermark Embedding
 Single Bit Encoding
 Genetic Algorithm Technique
 Pattern Search Technique
 Watermark Embedding Algorithm
Watermark Decoding Algorithms
 Decoding Threshold Evaluation
 Watermark Detection
 Attacker Model:
 Deletion Attack
 Alteration Attack
 Insertion Attack
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#8
19-04-2011, 01:05 PM


Watermarking relational database using optimization based techniques
Abstract:

For most corporations the volume of sensitive data used by outsourcing providers continues to increase. As the number of different entities having access to a database increases, it gets harder to prevent and trace-back data leakage. We address the problems of proving ownership and unauthorized data distribution (leakage) for relational databases. We propose three techniques that altogether may be used to detect, determine and trace-back data leaks from relational databases. We use business process outsourcing scenarios as the descriptive use case, but our techniques are equally applicable in other use cases when a relational database is shared among many parties and its confidentiality and authenticity needs to be protected. Previous work has shown how to watermark and fingerprint numerical relational data to prove ownership and track unauthorized redistributions respectively. We implemented a proof of concept implementation of our watermarking technique and showed by experimental results that our technique is resilient to tuple deletion, alteration, and insertion attacks.
A relational database is a database that groups data using common attributes found in the data set. The resulting "clumps" of organized data are much easier for people to understand.
For example, a data set containing all the real estate transactions in a town can be grouped by the year the transaction occurred; or it can be grouped by the sale price of the transaction; or it can be grouped by the buyer's last name; and so on.
Optimization:
Optimization is used to improving the performance. Here we are going to propose data hiding to prove the ownership effectively.
Existing System:
In existing system watermarking is not resilient to tuple deletion, alteration, and insertion attacks.
Proposed system:
In our proposed system we implemented encoding and decoding and data partition technique.
A data set is transformed into a watermarked version by applying a watermark encoding function that also takes as inputs a secret key only known to the copyright owner and a watermark.
Watermark decoding is the process of extracting the embedded watermark using the watermarked data set, the secret keys.
Data partitioning algorithm that partitions the data set based on a secret key. That is if your given correct secret key means watermark text will extract for modification (edit, update, delete).
This tool applies your watermarks to multiple files to protect your copyright. RealWatermark simplifies the process of creating and applying watermark to multiple image files in multiple folders. It supports a mixture of text, copyright symbols, graphic and drawing watermark of any complexity. The multi-level transparency setting allows you to choose and preview how your watermark will affect your image..
Why watemark is datamining?
1. Here we develop data hiding process
2. Here text consider as data(tuple)
Hardware Requirements:
• SYSTEM : Pentium III 700 MHz
• HARD DISK : 40 GB
• RAM : 128 MB
Software Requirements:
• Operating system :- Windows XP Professional
• Front End :- Microsoft Visual Studio .Net 2005
• Coding Language :- C# .Net
• BackEnd :-SqlServer 2000
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#9
25-05-2011, 11:48 AM

i wnt code immidiatly
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14-02-2012, 03:48 PM


to get information about the topic uses of watermarking full report ,ppt and related topic refer the link bellow

topicideashow-to-digital-watermarking--5450

topicideashow-to-watermarking-relational-databases-using-optimization-based-techniques?page=2

topicideashow-to-watermarking-schemes-evaluation

topicideashow-to-watermarking-digital-audio-full-report

topicideashow-to-watermarking-algorithm

topicideashow-to-digital-watermarking-full-seminar and presentation-report-download?page=15

topicideashow-to-digital-watermarking-full-seminar and presentation-report-download?pid=20966

topicideashow-to-content-dependent-water-marking-scheme-for-speech-signal
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23-04-2016, 09:10 PM

Hi, pls I need full project on watermarking relational database using optimization techniques with code.
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#12
13-07-2016, 04:13 PM

Abstract

Proving ownership rights on outsourced relational databases is a crucial issue in today's internet-based application environments and in many content distribution applications. In this paper, we present a mechanism for proof of ownership based on the secure embedding of a robust imperceptible watermark in relational data. We formulate the watermarking of relational databases as a constrained optimization problem and discuss efficient techniques to solve the optimization problem and to handle the constraints. Our watermarking technique is resilient to watermark synchronization errors because it uses a partitioning approach that does not require marker tuples. Our approach overcomes a major weakness in previously proposed watermarking techniques. Watermark decoding is based on a threshold-based technique characterized by an optimal threshold that minimizes the probability of decoding errors. We implemented a proof of concept implementation of our watermarking technique and showed by experimental results that our technique is resilient to tuple deletion, alteration, and insertion attacks.
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jaseela123
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#13
13-07-2016, 04:17 PM

This paper formulates the watermarking of relational database as a constrained optimization problem and to implement efficient techniques to handle the constraints like watermark synchronization errors and watermark detection. We present two techniques to solve the formulated optimization problem based on genetic algorithms (GAs) and pattern search (PS) techniques,and also a data partitioning technique that does not depend on marker tuples to locate the partitions and, thus, it is resilient to watermark synchronization errors.We develop an efficient technique for watermark detection that is based on an optimal threshold. The optimal threshold is selected by minimizing the probability of decoding error.
Watermarking 
Main Contribution 
 We formulate the watermarking of relational databases as a constrained optimization problem, and discuss efficient techniques to handle the constraints. We present two techniques to solve the formulated optimization problem based on genetic algorithms and pattern search techniques.
 We present a data partitioning technique that does not depend on marker tuples to locate the partitions and thus it is resilient to watermark synchronization errors.
 We develop an efficient technique for watermark detection that is based on an optimal threshold. The optimal threshold is selected by minimizing the probability of decoding error.
 With a proof of concept implementation of our watermarking technique, we have conducted experiments using both synthetic and real-world data. We have compared our watermarking technique with previous approaches shows the superiority of our technique with respect to all types of attacks. 

The rapid growth of internet and related technologies has offered an unprecedented ability to access and redistribute digital contents. In such a context, enforcing data ownership is an important requirement which requires articulated solutions, encompassing technical, organizational and legal aspects. Though we are still far from such comprehensive solutions, in the last years watermarking techniques have emerged as an important building block which plays a crucial role in addressing the ownership problem. Such techniques allow the owner of the data to embed an imperceptible watermark into the data. 

A watermark describes information that can be used to prove the ownership of data, such as the owner, origin, or recipient of the content. Secure embedding requires that the embedded watermark must not be easily tampered with, forged, or removed from the watermarked data. Imperceptible embedding means that the presence of the watermark is unnoticeable in the data. Furthermore, the watermark detection is blinded, that is, it neither requires the knowledge of neither the original data nor the watermark. Watermarking techniques have been developed for video, images, audio, and text data and also for software and natural language text.

By contrast the problem of watermarking relational data has not been given appropriate attention. There are, however, many application contexts for which data represent an important asset, the ownership of which must thus be carefully enforced. This is the case, for example, of weather data, stock market data, power consumption, consumer behavior data, medical and scientific data. 



Watermark embedding for relational data is made possible by the fact that real data can very often tolerate a small amount of error without any significant degradation with respect to their usability. For example when dealing with weather data, changing some daily temperatures of 1 or 2 degrees is a modification that leaves the data still usable.

To date only a few approaches to the problem of watermarking relational data have been proposed. These techniques, however, are not very resilient to watermark attacks. In this project and implimentation, we present a watermarking technique for relational data that is highly resilient compared to these techniques. In particular, our proposed technique is resilient to tuple deletion, alteration, and insertion attacks.
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