Data leakage detection Data leakage detection

ABSTRACTABSTRACTA data distributor has given sensitive data to a

set of supposedly trusted agents. Sometimes data is leaked and found in

unauthorized place e.g., on the web or on somebody's laptop.

Data leakage happens every day when confidential business information are leaked out.

When these are leaked out it leaves the company unprotected and goes outside the jurisdiction of the corporation.

MotivationMotivationIn the past few years ,there has been a

sharp increase in data leakage from many organizations.

According to 2006, the FBI computer crime and security survey, Data leakage is the greatest source of financial loss of organization.

The above issues motivated to me to choose this project.

ObjectiveObjective

The objective of this project is to improve the probability of identifying leakages using Data allocation strategies across the agents and also to identify the guilty party who leaked the data by injecting “realistic but fake” data records.

Problem StatementProblem Statement

In the course of doing business, sometimes sensitive data must be given to trusted third parties. Some of the data is leaked and found in an unauthorized place.

The distributor cannot blame the agent without any evidence. This project identifies the agent who leaked the data with enough evidence.

  Limitations of current systemLimitations of current system

Current approach can detect the hackers

but the total number of evidence will be less and the organization may not be able to proceed legally for further proceedings due to lack of good amount of evidence and the chances to escape of hackers are high.

Proposed system Proposed system addresses addresses following issuesfollowing issues1. Algorithm used to distribute the objects

to agents that improves the chances of identifying a leaker.

2. Realistic but fake objects is injected to the distributed set.

3. Leakers cannot argue that they did not leak the confidential data, because this system traces leakers with good amount of evidence.

Block diagramBlock diagram

Request data

Leaks the data

Distributor

Agent

Database

View Data to transfer the

agents

Add the fake objects to the original data

Find the guilty agents

Probability distribution of data leaked by guilty agents

Login registration

Explicit Data request

Transfer data to agents

E-Random(Algorith

m)

E-Optimal(Algorithm

)

ModulesModules

1. Data allocation module

2. Fake object module

3. Optimization module

4. Data distributor module

Data Allocation Module:Data Allocation Module:The main focus of our project is

the data allocation problem as how can the distributor “intelligently” give data to agents in order to improve the chances of detecting a guilty agent.

Fake Object Module:Fake Object Module:

Fake objects are objects generated by the distributor in order to increase the chances of detecting agents that leak data. The distributor may be able to add fake objects to the distributed data in order to improve his effectiveness in detecting guilty agents. Our use of fake objects is inspired by the use of “trace” records in mailing lists.

Optimization Module:Optimization Module:

The Optimization Module is the distributor’s data allocation to agents has one constraint and one objective. The distributor’s constraint is to satisfy agents’ requests, by providing them with the number of objects they request or with all available objects that satisfy their conditions. His objective is to be able to detect an agent who leaks any portion of his data.

Data Distributor:Data Distributor:

A data distributor has given sensitive data to a set of supposedly trusted agents (third parties). Some of the data is leaked and found in an unauthorized place (e.g., on the web or somebody’s laptop). 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.

Software &Hardware Software &Hardware RequirementsRequirementsHardware Required:

  System : Pentium IV 2.4 GHz

Hard Disk : 40 GB

Floppy Drive : 1.44 MB

RAM : 256 MB

 Software Required:

O/S : Windows XP.

Language : J2EE

Data Base : MySql Server

 

 

 

ReferencesReferences P. Papadimitriou and H. Garcia-molina “Data leakage

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P.M Pardalos and S.A Vavasis,”Quadratic programming with one negative Eigen value is NP-Hard,” J. Global Optimization. Vol 1, no 1, pp.

IEEE conference paper: Agrawal and J. Kiernan. Watermarking relational databases. In VLDB ’02: Proceedings of the 28th international conference on Very Large Data Bases, pages 155–166. VLDB Endowment, 2002

Y. Cui and J. Widom. Lineage tracing for general data warehouse transformations. In The VLDB Journal, pages 471–480, 2001.