Data Leakage Detection

  • Price ₹8,000.00




Data Leakage Detection Project propose data allocation strategies that improve the probability of identifying leakages. 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. We also present algorithms for distributing objects to agents, in a way that improves our chances of identifying a leaker. Finally, we also consider the option of adding “fake” objects to the distributed set. Such objects do not correspond to real entities but appear realistic to the agents. In a sense, the fake objects act as a type of watermark for the entire set, without modifying any individual members. If it turns out that an agent was given one or more fake objects that were leaked, then the distributor can be more confident that agent was guilty.

 

Hardware Requirements:

·       System : Pentium IV 2.4 GHz

 

·       Hard Disk : 40 GB

 

·       Floppy Drive : 1.44 MB

 

·       Monitor :15 VGA colour

 

·       RAM : 256 MB

 

Software Requirements:

 

·         Language : JAVA, JavaScript.

 

·         Front End : JSP, Servlet.

 

·         Back End : MySQL.

 

·         Web server : Apache Tomcat 7.0.

 

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. The main focus of this paper is the data allocation problem: how can the distributor “intelligently” give data to agents in order to improve the chances of detecting a guilty agent. There are four instances of this problem we address, depending on the type of data requests made by agents and whether “fake objects” are allowed. The two types of requests we handle sample and explicit. Fake objects are objects generated by the distributor that are not in set. The objects are designed to look like real objects, and are distributed to agents together with T objects, in order to increase the chances of detecting agents that leak data.

 

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. The distributor may be able to add fake objects to the distributed data in order to improve his effectiveness in detecting guilty agents. However, fake objects may impactthe correctness of what agents do, so they may not always be allowable.

 

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. 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 heir conditions.

 

Advantages:

 

·         If the distributor sees “enough evidence” that an agent leaked data, he may stop doing business with him, or may initiate legal proceedings.

·         In this project we develop a model for assessing the “guilt” of agents.

·         We also present algorithms for distributing objects to agents, in a way that improves our chances of identifying a leaker.

·         Finally, we also consider the option of adding “fake” objects to the distributed set. Such objects do not correspond to real entities but appear.

·         If it turns out an agent was given one or more fake objects that were leaked, then the distributor can be more confident that agent was guilty.

 

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