A Novel Mechanism for Fast Detection of Transformed Data Leakage
A Novel Mechanism for Fast Detection of Transformed Data Leakage java project report The computer system poses a serious threat to the organisational security due to the leak of sensitive data. According to the report of risk based security (RBS), the leaked sensitive data records has increased dramatically during last few years, (i.e.) from 412 million in 2012 to 822 million in 2013. These are caused only by the lack of proper encryption on files and documents and by human errors these causes data loss.
Organization has the responsibility of screening the content which is stored in the system as sensitive data. In this paper, we utilize two techniques, which are levenshtein-distance technique and luecene search framework. These two helps to detect the leakage of data and this technique is used for screening the data which are outsourced and it also keep an track of, who is transferring the data.
H/W System Configuration:-
System : I3 Processor.
Hard Disk : 500 GB.
Monitor : 15’’ LED
Input Devices : Keyboard, Mouse
Ram : 4 GB
S/W System Configuration:-
Operating system : Windows 7/UBUNTU.
Coding Language : Java 1.7 ,Hadoop 0.8.1
IDE : Eclipse
Database : MYSQL
A Novel Mechanism for Fast Detection of Transformed Data Leakage java project report Fast detection of data-leakage framework to avoid sensitive data exposure and also provide privacy preserving to sensitive data. Lucerne search framework to detect the sensible data easily using indexing technique. Levenshtein distance algorithm to detect the shuffling of transferred mail content. To implement the own logics for detect sampling of transferred mail content appropriately. We implement threshold rate based on assigning and checking domains based user filtering technique. In future it will be created with the deleting of request list if it is checked or proceeded once.
Name of the Project : A Novel Mechanism for Fast Detection of Transformed Data Leakage
Project Cost : $ 50
Delivery Time : Within 48 hours
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