Browsing by Author "Chaudhary, N.K."

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    Comparative evaluation of algorithms for effective data leakage detection
    (2013) Kumar, A.; Goyal, A.; Kumar, A.; Chaudhary, N.K.; Sowmya, Kamath S.
    Researchers have proposed several mechanisms to secure data from unauthorized use but there is very less work in the field of detecting and managing an authorized or trustworthy agent that has caused a data leak to some third party advertently or unknowingly. In this paper, we implement methods aimed at improving the odds of detecting such leakages when a distributer's sensitive data has been leaked by trustworthy agents and also to possibly identify the agent(s) that leaked the data. We also implement some data allocation strategies that can improve the probability of identifying leakages and can also be used to assess the likelihood of a leak at a particular agent assuming the fact that the data was not simply guessed by the third party where the leaked data set has been found. We also propose new allocation strategies that work on the basis of No-Wait model, i.e. agent does not need to wait for other agents' allocation and it is different from already proposed model that makes an agent wait for others. These methods do not rely on the alterations of the distributed data, but rather focus on minimizing the overlapping of the allocated data items to various agents, thus facilitating an exact determination of the guilty agent in a particular data leakage scenario. � 2013 IEEE.
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    Comparative evaluation of algorithms for effective data leakage detection
    (2013) Kumar, A.; Goyal, A.; Kumar, A.; Chaudhary, N.K.; Kamath S․, S.S.
    Researchers have proposed several mechanisms to secure data from unauthorized use but there is very less work in the field of detecting and managing an authorized or trustworthy agent that has caused a data leak to some third party advertently or unknowingly. In this paper, we implement methods aimed at improving the odds of detecting such leakages when a distributer's sensitive data has been leaked by trustworthy agents and also to possibly identify the agent(s) that leaked the data. We also implement some data allocation strategies that can improve the probability of identifying leakages and can also be used to assess the likelihood of a leak at a particular agent assuming the fact that the data was not simply guessed by the third party where the leaked data set has been found. We also propose new allocation strategies that work on the basis of No-Wait model, i.e. agent does not need to wait for other agents' allocation and it is different from already proposed model that makes an agent wait for others. These methods do not rely on the alterations of the distributed data, but rather focus on minimizing the overlapping of the allocated data items to various agents, thus facilitating an exact determination of the guilty agent in a particular data leakage scenario. © 2013 IEEE.