Core IEEE Projects (Division of Conceptz)

#108,5th Main, 4th Cross, Hanumanth Nagar, Basavanagudi, Bangalore-50, Website: contact: 9535052050

Data Leakage Detection

ABSTRACT:

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. We propose data allocation strategies (across the agents) that improve the probability of identifying leakages. These methods do not rely on alterations of the released data (e.g., watermarks). In some cases we can also inject “realistic but fake” data records to further improve our chances of detecting leakage and identifying the guilty party.

EXISTING SYSTEM:

Traditionally, leakage detection is handled by watermarking,e.g., a unique code is embedded in each distributedcopy. If that copy is later discovered in thehands of an unauthorized party, the leaker can be identified.Watermarks can be very useful in some cases, butagain, involve some modification of the original data.Furthermore, watermarks can sometimes be destroyedif the data recipient is malicious.E.g. A hospital may give patient recordsto researchers who will devise new treatments. Similarly,a company may have partnerships with other companiesthat require sharing customer data. Another enterprisemay outsource its data processing, so data must be givento various other companies.We call the owner of the datathe distributor and the supposedly trusted third partiesthe agents.

PROPOSED SYSTEM:

Our goal is to detect when the distributor’ssensitive data has been leaked by agents, and if possibleto identify the agent that leaked the data.Perturbation is a very usefultechnique where the data is modified and made “lesssensitive” before being handed to agents. we developunobtrusive techniques for detectingleakage of a set of objects or records.

In this section we develop a model for assessing the“guilt” of agents. We also present algorithms for distributingobjects to agents, in a way that improves ourchances of identifying a leaker. Finally, we also considerthe option of adding “fake” objects to the distributed set.Such objects do not correspond to real entities but appearrealistic to the agents. In a sense, the fake objects acts as atype of watermark for the entire set, without modifyingany individual members. If it turns out an agent wasgiven one or more fake objects that were leaked, then thedistributor can be more confident that agent was guilty.

Problem Setup and Notation:

A distributor owns a set T={t1,…,tm}of valuabledata objects. The distributor wants to share some of theobjects with a set of agents U1,U2,…Un, but does notwish the objects be leaked to other third parties. Theobjects in T could be of any type and size, e.g., theycould be tuples in a relation, or relations in a database.An agent Ui receives a subset of objects,determined either by a sample request or an explicitrequest:

1.Sample request

2.Explicit request

Guilt Model Analysis:

our model parameters interact andto check if the interactions match our intuition, in thissection we study two simple scenarios as Impact of Probability p and Impact of Overlap between Ri and S. In each scenariowe have a target that has obtained all the distributor’sobjects, i.e., T = S.

Algorithms:

1. Evaluation of Explicit Data Request Algorithms

In the first place, the goal of these experiments was tosee whether fake objects in the distributed data sets yieldsignificant improvement in our chances of detecting aguilty agent. In the second place, we wanted to evaluateour e-optimal algorithm relative to a random allocation.

2. Evaluation of Sample Data Request Algorithms

With sample data requests agents are not interested inparticular objects. Hence, object sharing is not explicitlydefined by their requests. The distributor is “forced” toallocate certain objects to multiple agents only if thenumber of requested objects exceeds the numberof objects in set T. The more data objects the agentsrequest in total, the more recipients on average an objecthas; and the more objects are shared among differentagents, the more difficult it is to detect a guilty agent.

MODULES:

1. Data Allocation Module:

The main focus of our project is the data allocation problem as how can the distributor “intelligently” give data toagents in order to improve the chances of detecting aguilty agent.

2. Fake Object Module:

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

3. Optimization Module:

The Optimization Moduleis the distributor’s data allocation to agents has one constraintand one objective. The distributor’s constraint isto satisfy agents’ requests, by providing them with thenumber of objects they request or with all availableobjects that satisfy their conditions. His objective is to beable to detect an agent who leaks any portion of his data.

4. Data Distributor:

A data distributor has given sensitive data to a set of supposedly trusted agents (thirdparties). Some of the data is leaked and found in an unauthorized place (e.g., on the web or somebody’s laptop). The distributor mustassess the likelihood that the leaked data came from one or more agents, as opposed to having been independently gathered by othermeans.

Hardware Required:

System:Pentium IV 2.4 GHz

Hard Disk : 40 GB

Floppy Drive: 1.44 MB

Monitor: 15 VGA colour

Mouse: Logitech.

Keyboard: 110 keys enhanced.

RAM: 256 MB

Software Required:

O/S :Windows XP.

Language :Asp.Net, c#.

Data Base :Sql Server 2005

Tags: Core IEEE Projects,ieee Projects, ieee Projects 2011-12,ieee projects for cse, ieee projects for cse 2011, ieee projects 2011, ieee projects 2011 in data mining, ieee projects 2011 on image processing, ieee projects 2011 topics, ieee projects 2011 list, ieee projects 2011 for cse in java, ieee projects 2011 for it, ieee projects 2011 for mca, ieee projects 2011 for computer science, ieee projects on cloud computing, ieee projects 2011 on networking, ieee projects on networking and network security, ieee projects 2011 in Bangalore, ieee projects in java, ieee projects in .net, ieee projects in asp.net, ieee projects in Bangalore, ieee Academic Projects, ieee, ieee Projects Bangalore, ieee Software Projects, Latest IEEE Projects, IEEE Student Projects, IEEE Final year Student Projects, Final Year Projects, ENGINEERING PROJECTS, MCA projects, BE projects, BCA Projects, JAVA projects, J2EE projects, .NET projects, Students projects, ieee Projects in Bangalore, M-tech Internship in Company-tech Projects in Bangalore, Real Time Projects.