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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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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