CONTENTS

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CONTENTS

• ABSTRACT
• INTRODUCTION
• OBJECTIVES
• STUDY AND ANALYSIS
• FLOW CHART
• FUTURE SCOPE
• LIMITATIONS
• APPLICATIONS
• CONCLUSION
• REFERENCES

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ABSTRACT

• A data distributor has given sensitive data to a
  set of supposedly trusted agents. Some of the
  data are leaked and found in an unauthorized
  place.
• The distributor must assess the likelihood that
  the leaked data came from one or more agents, as
  opposed to having been independently gathered by
  other means.
• We propose data allocation strategies that
  improve the probability of identifying leakages.
• These methods do not rely on alterations of the
  released data (e.g., watermarks).
•  

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INTRODUCTION

• DISTRIBUTER He is the owner of the data who
  distributes the data to the third parties.
• THIRD PARTIES Trusted recipients of the
  distributers data who are also called as agents.
• PERTURBATION Technique where the data are
  modified and made less sensitive before being
  handed to agents.
• ALLOCATION STRATEGIES Tactics used by the
  distributer to allocate the sensitive data in
  order to increase the probability of detecting
  the data leakage.

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OBJECTIVES

• Avoiding the perturbation of the original data
  before being handed to the agents.
• Detecting if the distributers sensitive data has
  been leaked by the agents.
• The likelihood that an agent is responsible for a
  leak is assessed.

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STUDY AND ANALYSIS

• EXISTING SYSTEM
• Traditionally, leakage detection is handled by
  watermarking, e.g., a unique code is embedded in
  each distributed copy.
• If that copy is later discovered in the hands of
  an unauthorized party, the leaker can be
  identified.
• DRAWBACKS OF EXISTING SYSTEM
• Watermarking involves some modification of the
  original data.
• Watermarks can sometimes be destroyed if the data
  recipient is intelligent.

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• PROPOSED SYSTEM
• ALLOCATION STRATEGIES
• The proposed system uses two allocation
  strategies through which the data is allocated to
  the agents. They are,
• Sample request RiSAMPLE (T, mi) Any subset of
  mi records from T can be given to agent.
• Explicit request RiEXPLICIT (T, condition)
  Agent receives all T objects that satisfy
  condition.

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start
FLOW CHART
Users explicit request
Check the Condition Select the agent.
else
exit
Create Fake Object is
Invoked
User Receives the Output.
Loop Iterates
end
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• Example
• Say that T contains customer records for a given
  company A. Company A hires a marketing agency U1
  to do an online survey of customers.
• Since any customers will do for the survey, U1
  requests a sample of 1,000 customer records.
• At the same time, company subcontracts with
  agent U2 to handle billing for all California
  customers.
• Thus, U2 receives all T records that satisfy the
  condition state is California.

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FUTURE SCOPE

• Future work includes the investigation of agent
  guilt models that capture leakage
• scenarios.
• The extension of data allocation strategies so
  that they can handle agent requests in an online
  fashion.

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LIMITATION

• The presented strategies assume that there is a
  fixed set of agents with requests known in
  advance.
• The distributor may have a limit on the number of
  fake objects.

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APPLICATIONS

• It helps in detecting whether the distributers
  sensitive data has been leaked by the trustworthy
  or authorized agents.
• It helps to identify the agents who leaked the
  data.
• Reduces cybercrime.

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CONCLUSION

• Though the leakers are identified using the
  traditional technique of watermarking, certain
  data cannot admit watermarks.
• In spite of these difficulties, we have shown
  that it is possible to assess the likelihood that
  an agent is responsible for a leak.
• We have shown that distributing data judiciously
  can make a significant difference in identifying
  guilty agents using the different data allocation
  strategies.

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REFERENCES

• 1 P. Buneman and W.-C. Tan, Provenance in
  Databases, Proc. ACM SIGMOD, pp. 1171-1173,
  2007.
• 2 Y. Cui and J. Widom, Lineage Tracing for
  General Data Warehouse Transformations, The VLDB
  J., vol. 12, pp. 41-58, 2003.
• 3 S. Czerwinski, R. Fromm, and T. Hodes,
  Digital Music Distribution and Audio
  Watermarking, http//www.scientificcommons.
  org/43025658, 2007.
• 4 F. Guo, J. Wang, Z. Zhang, X. Ye, and D. Li,
  An Improved Algorithm to Watermark Numeric
  Relational Data, Information

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THANK YOU ?