CREATE DATABASES WATERMARKING
ABSTRACT: 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. 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: T ! rapid growth of the "nternet and related technologies has offered an unprecedented ability to access and redistribute digital contents. "n such a context, enforcing data ownership is an important requirement, which requires articulated solutions, encompassing technical, organizational, and legal aspects. #lthough we are still far from such comprehensive solutions, in the last years, watermarking techniques have emerged as an important building block that plays a crucial role in addressing the ownership problem. $uch techniques allow the owner of the data to embed an imperceptible watermark into the data. # watermark describes information that can be used to prove the ownership of data such as the owner, origin, or recipient of the content. $ecure embedding requires that the embedded watermark must not be easily tampered with, forged, or removed from the watermarked data. "mperceptible embedding means that the presence of the watermark is unnoticeable in the data. %urthermore, the watermark detection is blinded, that is, it neither requires the knowledge of the original data nor the watermark. Watermarking techniques have been developed for video, images, audio, and text data and also for software and natural language text. &y contrast, the problem of watermarking relational data has not been given appropriate attention. There are, however, many application contexts for which data represent an important asset, the ownership of which must thus be carefully enforced. This is the case, for example, of weather data, stock market data, power consumption, consumer behavior data, and medical and scientific data. Watermark embedding for relational data is made possible by the fact that real data can very often tolerate a small amount of error without any significant degradation with respect to their usability. %or example, when dealing with weather data, changing some daily temperatures of ' or( degrees is a modification that leaves the data still usable. To date, only a few approaches to the problem of watermarking relational data have been proposed. These techniques, however, are not very resilient to watermark attacks. "n this paper, we present a watermarking technique for relational data that is
highly resilient compared to these techniques. "n particular, our proposed technique is resilient to tuple deletion, alteration, and insertion attacks. We formulate the watermarking of relational databases as a constrained optimization problem and discuss efficient techniques to handle the constraints. We present two techniques to solve the formulated optimization problem based on genetic algorithms )*#s+ and pattern search ),$+ 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.
SYSTEM STUDY
2. SYSTEM STUDY 2.1 EXISTING SYSTEM: Existing Syste : Watermarking in least significant bits)-$&+.This technique embeds the watermark bits in the least significant bits of selected attributes of a selected subset of tuple.s. "t uses secret key in watermarking. %or each tuple.s a secure message, authenticated code is computed using the secret key and tuple.s primary key. The computed /#0 is used select candidate tuple.s attributes and the -$& positions in the selected attributes.
2.2 !RO!OSED SYSTEM: !"#$#se% Syste : Watermarking embeds ownership information in digital content. Watermark describes information tat can be used to prove the ownership of relational database. the embedding is hidden tat the presence of watermarking is invisible to the user. ere
2.& 'EASIBI(ITY STUDY #ll pro1ects are feasible given unlimited resources and infinite time. "t is both necessary and prudent to evaluate the feasibility of the pro1ect at the earliest possible time. %easibility and risk analysis is related in many ways. "f pro1ect risk is great , the feasibility listed below is equally important. The following feasibility techniques has been used in this pro1ect • • • 2perational %easibility Technical %easibility !conomic %easibility
2perational %easibility3 ,roposed system is beneficial since it turned into information system analyzing the traffic that will meet the organizations operating requirements. "4 security, the file is transferred to the destination and the acknowledgement is given to the server. &ulk of data transfer is sent without traffic. Technical %easibility3 Technical feasibility centers on the existing computer system )hardware , software, etc..+ and to what extent it can support the proposed addition. %or example, if the current computer is operating at 567 capacity. This involves, additional hardware )8#/ and ,820!$$28+ will increase the speed of the process. "n software, 2pen $ource language that is 9#:# and 9/% is used. We can also use in -inux operating system. The technical requirement for this pro1ect are 9ava tool kit and $wing component as software and normal hardware configuration is enough , so the system is more feasible on this criteria.
!conomic %easibility3 !conomic feasibility is the most frequently used method for evaluating the effectiveness of a candidate system. /ore commonly known as cost ; benefit analysis, the procedure is to determine the benefits and saving that are expected from a candidate and compare them with the costs. "f the benefits outweigh cost. Then the decision is made to design and implement the system. 2therwise drop the system. This system has been implemented such that it can be used to analysis the traffic. $o it does not requires any extra equipment or hardware to implement. $o it is economically feasible to use.
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SYSTEM S!ECI'ICATION . SYSTEM S!ECI'ICATION &.1 )ARDWARE S!ECI'ICATION: ,rocessor $peed 8#/ ard =isk 3 *eneral 3 3 ,entium-""" 3 3 >6*& ?ey&oard, /onitor , /ouse '.'* z
ABSTRACT: 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. 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: T ! rapid growth of the "nternet and related technologies has offered an unprecedented ability to access and redistribute digital contents. "n such a context, enforcing data ownership is an important requirement, which requires articulated solutions, encompassing technical, organizational, and legal aspects. #lthough we are still far from such comprehensive solutions, in the last years, watermarking techniques have emerged as an important building block that plays a crucial role in addressing the ownership problem. $uch techniques allow the owner of the data to embed an imperceptible watermark into the data. # watermark describes information that can be used to prove the ownership of data such as the owner, origin, or recipient of the content. $ecure embedding requires that the embedded watermark must not be easily tampered with, forged, or removed from the watermarked data. "mperceptible embedding means that the presence of the watermark is unnoticeable in the data. %urthermore, the watermark detection is blinded, that is, it neither requires the knowledge of 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. &y 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, and 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. %or example, when dealing with weather data, changing some daily temperatures of ' or( 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. "n this paper, we present a watermarking technique for relational data that is
highly resilient compared to these techniques. "n particular, our proposed technique is resilient to tuple deletion, alteration, and insertion attacks. 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 )*#s+ 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.
SYSTEM STUDY
2. SYSTEM STUDY 2.1 EXISTING SYSTEM: Existing Syste : Watermarking in least significant bits)-$&+.This technique embeds the watermark bits in the least significant bits of selected attributes of a selected subset of tuple.s. "t uses secret key in watermarking. %or each tuple.s a secure message, authenticated code is computed using the secret key and tuple.s primary key. The computed /#0 is used select candidate tuple.s attributes and the -$& positions in the selected attributes.
2.2 !RO!OSED SYSTEM: !"#$#se% Syste : Watermarking embeds ownership information in digital content. Watermark describes information tat can be used to prove the ownership of relational database. the embedding is hidden tat the presence of watermarking is invisible to the user. ere
2.& 'EASIBI(ITY STUDY #ll pro1ects are feasible given unlimited resources and infinite time. "t is both necessary and prudent to evaluate the feasibility of the pro1ect at the earliest possible time. %easibility and risk analysis is related in many ways. "f pro1ect risk is great , the feasibility listed below is equally important. The following feasibility techniques has been used in this pro1ect • • • 2perational %easibility Technical %easibility !conomic %easibility
2perational %easibility3 ,roposed system is beneficial since it turned into information system analyzing the traffic that will meet the organizations operating requirements. "4 security, the file is transferred to the destination and the acknowledgement is given to the server. &ulk of data transfer is sent without traffic. Technical %easibility3 Technical feasibility centers on the existing computer system )hardware , software, etc..+ and to what extent it can support the proposed addition. %or example, if the current computer is operating at 567 capacity. This involves, additional hardware )8#/ and ,820!$$28+ will increase the speed of the process. "n software, 2pen $ource language that is 9#:# and 9/% is used. We can also use in -inux operating system. The technical requirement for this pro1ect are 9ava tool kit and $wing component as software and normal hardware configuration is enough , so the system is more feasible on this criteria.
!conomic %easibility3 !conomic feasibility is the most frequently used method for evaluating the effectiveness of a candidate system. /ore commonly known as cost ; benefit analysis, the procedure is to determine the benefits and saving that are expected from a candidate and compare them with the costs. "f the benefits outweigh cost. Then the decision is made to design and implement the system. 2therwise drop the system. This system has been implemented such that it can be used to analysis the traffic. $o it does not requires any extra equipment or hardware to implement. $o it is economically feasible to use.
.
SYSTEM S!ECI'ICATION . SYSTEM S!ECI'ICATION &.1 )ARDWARE S!ECI'ICATION: ,rocessor $peed 8#/ ard =isk 3 *eneral 3 3 ,entium-""" 3 3 >6*& ?ey&oard, /onitor , /ouse '.'* z