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01-Feb-12 Data Leakage Detection 01-Feb-12 Data Leakage Detection

01-Feb-12 Data Leakage Detection - PowerPoint Presentation

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01-Feb-12 Data Leakage Detection - PPT Presentation

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

leakage data feb detection data leakage detection feb agents agent strategies allocation leaked sensitive condition records requests detecting customers

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Slide1

01-Feb-12

Data Leakage Detection

1Slide2

CONTENTS

ABSTRACT

INTRODUCTION

OBJECTIVES

STUDY AND ANALYSIS

FLOW CHART

FUTURE SCOPELIMITATIONSAPPLICATIONSCONCLUSIONREFERENCES

01-Feb-12

2

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).

 

01-Feb-12

3

Data Leakage DetectionSlide4

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.

01-Feb-12

4

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

5

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.

01-Feb-12

6

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.

01-Feb-12

7

Data Leakage DetectionSlide8

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: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.”

01-Feb-12Data Leakage Detection9Slide10

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.

01-Feb-12

11Data Leakage DetectionSlide12

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.

01-Feb-12

13

Data Leakage DetectionSlide14

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

01-Feb-12

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Data Leakage Detection