data leakage detection
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Data Leakage Detection 101-Feb-12
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 recipient’s of the distributer’s 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 distributer’s 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 Ri=SAMPLE (T, mi): Any subset of mi records from T can be given to agent.
Explicit request Ri=EXPLICIT (T, condition): Agent receives all T objects that satisfy condition.
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01-Feb-12 Data Leakage Detection 8
start
User’s explicit request
Check the Condition Select the
agent.
Create Fake Object is Invoked
User Receives the Output.
end
Loop Iterates
exit else
FLOW CHART:
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 distributer’s 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
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