Balamurugan M | Anna University (original) (raw)
Phone: +91 9047430101
Address: Associate Professor & Head,
Department of CSE,
Arulmurugan College of Engineering,
Karur, Tamilnadu, India - 639 206.
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Papers by Balamurugan M
Privacy is a critical requirement in distributed data mining. Cryptography-based secure multipart... more Privacy is a critical requirement in distributed data mining. Cryptography-based secure multiparty computation is a main approach for privacy preserving. However, it shows poor performance in large scale distributed systems. Meanwhile, data transformation techniques are comparatively efficient but are mainly used in centralized privacy preserving data mining (PPDM). The major challenge of data transformation is to achieve the desired balance between the level of privacy guarantee and the level of data utility. Data privacy and data utility are commonly considered as a pair of conflicting requirements in privacy preserving data mining systems and applications. Multiplicative perturbation algorithms aim at improving data privacy while maintaining the desired level of data utility by selectively preserving the mining task and model specific information during the data perturbation process. By preserving the task and model specific information, a set of "transformation-invariant data mining models" can be applied to the perturbed data directly, achieving the required model accuracy. Often a multiplicative perturbation algorithm may find multiple data transformations that preserve the required data utility.
Problem statement: In the current modern business environment, its success is defined by collabor... more Problem statement: In the current modern business environment, its success is defined by collaboration, team efforts and partnership, rather than lonely spectacular individual efforts in isolation. So the collaboration becomes especially important because of the mutual benefit it brings. Sometimes, such collaboration even occurs among competitors, or among companies that have conflict of interests, but the collaborators are aware that the benefit brought by such collaboration will give them an advantage over other competitors. Approach: For this kind of collaboration, data's privacy becomes extremely important: all the parties of the collaboration promise to provide their private data to the collaboration, but neither of them wants each other or any third party to learn much about their private data. One of the major problems that accompany with the huge collection or repository of data is confidentiality. The need for privacy is sometimes due to law or can be motivated by business interests. Results: Performance of privacy preserving collaborative data using secure multiparty computation is evaluated with attack resistance rate measured in terms of time, number of session and participants and memory for privacy preservation. Conclusion: Privacy-preserving data mining considers the problem of running data mining algorithms on confidential data that is not supposed to be revealed, even to the party running the algorithm.
Privacy is a critical requirement in distributed data mining. Cryptography-based secure multipart... more Privacy is a critical requirement in distributed data mining. Cryptography-based secure multiparty computation is a main approach for privacy preserving. However, it shows poor performance in large scale distributed systems. Meanwhile, data transformation techniques are comparatively efficient but are mainly used in centralized privacy preserving data mining (PPDM). The major challenge of data transformation is to achieve the desired balance between the level of privacy guarantee and the level of data utility. Data privacy and data utility are commonly considered as a pair of conflicting requirements in privacy preserving data mining systems and applications. Multiplicative perturbation algorithms aim at improving data privacy while maintaining the desired level of data utility by selectively preserving the mining task and model specific information during the data perturbation process. By preserving the task and model specific information, a set of "transformation-invariant data mining models" can be applied to the perturbed data directly, achieving the required model accuracy. Often a multiplicative perturbation algorithm may find multiple data transformations that preserve the required data utility.
Problem statement: In the current modern business environment, its success is defined by collabor... more Problem statement: In the current modern business environment, its success is defined by collaboration, team efforts and partnership, rather than lonely spectacular individual efforts in isolation. So the collaboration becomes especially important because of the mutual benefit it brings. Sometimes, such collaboration even occurs among competitors, or among companies that have conflict of interests, but the collaborators are aware that the benefit brought by such collaboration will give them an advantage over other competitors. Approach: For this kind of collaboration, data's privacy becomes extremely important: all the parties of the collaboration promise to provide their private data to the collaboration, but neither of them wants each other or any third party to learn much about their private data. One of the major problems that accompany with the huge collection or repository of data is confidentiality. The need for privacy is sometimes due to law or can be motivated by business interests. Results: Performance of privacy preserving collaborative data using secure multiparty computation is evaluated with attack resistance rate measured in terms of time, number of session and participants and memory for privacy preservation. Conclusion: Privacy-preserving data mining considers the problem of running data mining algorithms on confidential data that is not supposed to be revealed, even to the party running the algorithm.