1 CONTENTS ABSTRACT INTRODUCTION OBJECTIVES STUDY AND ANALYSIS FLOW CHART FUTURE SCOPE LIMITATIONS APPLICATIONS CONCLUSION REFERENCES 01Feb12 2 Data Leakage Detection ABSTRACT A data ID: 713102
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01-Feb-12
Data Leakage Detection
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CONTENTS
ABSTRACT
INTRODUCTION
OBJECTIVES
STUDY AND ANALYSIS
FLOW CHART
FUTURE SCOPELIMITATIONSAPPLICATIONSCONCLUSIONREFERENCES
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Data Leakage DetectionSlide3
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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Data Leakage DetectionSlide5
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.01-Feb-12
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Data Leakage DetectionSlide6
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 SYSTEMWatermarking involves some modification of the original data.Watermarks can sometimes be destroyed if the data recipient is intelligent.
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Data Leakage DetectionSlide7
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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Data Leakage DetectionSlide8
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Data Leakage Detection
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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:Slide9
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.01-Feb-1210
Data Leakage DetectionSlide11
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.
01-Feb-1212Data Leakage DetectionSlide13
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,” Information01-Feb-1214Data Leakage DetectionSlide15
THANK YOU
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Data Leakage Detection