Browsing by Author "Sudheesh, R.K."

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Efficient privacy preserving ranked search over encrypted data
(2016) Praseed, A.; Sudheesh, R.K.; Chandrasekaran, K.
Cloud computing and its ever so increasing prominence has rendered it as an unavoidable component for data storage and other data services. The security challenges of storing sensitive data on the cloud is reduced to an extent by the Encryption of data, though in the process of encrypted data search, efficiency is compromised. The encrypted data on the cloud can be retrieved using Searchable Symmetric Encryption (SSE). The current work uses multi-keyword searchable encryption scheme with top-k retrieval to avoid compromises on data privacy occurred by using Order Preserving Encryption schemes. The encryption scheme uses homomorphic encryption and vector space model. The vector space model provides the required search accuracy. The homomorphic encryption allows majority of the computation to be done at the server side while concealing the sensitive data. The user alone can identify the final result of the relevance calculation and request for the actual file. In this paper, phrase searching is included to improve the search results on the encrypted data. To accomplish this we maintain a list of the keyword locations in the encrypted file index. The cloud server, which we assume to be honest-but-curious, operates on these encrypted values and identifies if the words occur in close proximity without knowing the actual locations of these words and the words itself. � 2015 IEEE.
Efficient privacy preserving ranked search over encrypted data
(Institute of Electrical and Electronics Engineers Inc., 2016) Praseed, A.; Sudheesh, R.K.; Chandrasekaran, K.
Cloud computing and its ever so increasing prominence has rendered it as an unavoidable component for data storage and other data services. The security challenges of storing sensitive data on the cloud is reduced to an extent by the Encryption of data, though in the process of encrypted data search, efficiency is compromised. The encrypted data on the cloud can be retrieved using Searchable Symmetric Encryption (SSE). The current work uses multi-keyword searchable encryption scheme with top-k retrieval to avoid compromises on data privacy occurred by using Order Preserving Encryption schemes. The encryption scheme uses homomorphic encryption and vector space model. The vector space model provides the required search accuracy. The homomorphic encryption allows majority of the computation to be done at the server side while concealing the sensitive data. The user alone can identify the final result of the relevance calculation and request for the actual file. In this paper, phrase searching is included to improve the search results on the encrypted data. To accomplish this we maintain a list of the keyword locations in the encrypted file index. The cloud server, which we assume to be honest-but-curious, operates on these encrypted values and identifies if the words occur in close proximity without knowing the actual locations of these words and the words itself. Â© 2015 IEEE.
Study of malignancy associated changes in sputum images as an indicator of lung cancer
(2017) Sudheesh, R.K.; Rajan, J.; Veena, V.S.; Sujathan, K.
Lung cancer is one among the major causes of cancer related deaths. Fortunately, an early stage diagnosis can increase the survival rates of the patients. Sputum cytology is one of the easiest and cost-effective method for lung cancer diagnosis. Chances of misdiagnosis and sampling error related to sputum cytology led to the concept of malignancy associated changes. Malignancy associated changes (MAC) are the subtle changes that happens to the normal appearing cells near or distant from the malignant cells. Literature suggests that these changes can be used as an indicator for lung cancer rather than using malignant cells which are very less in number compared to the normal appearing cells in sputum cytology images. The proposed work is intended to detect cells with MAC from sputum smear images. Analysis of nuclei texture features of sputum cell nuclei using Gray Level Co-occurrence Matrix and Gray Level Run Length Matrix from both normal and cancer patients revealed that both type of cells could be differentiated. Among 110 texture features calculated for each nuclei, a set of 35 features which clearly distinguishes normal cells and normal appearing cells were chosen. Support Vector Machine (SVM) classifier is used to classify the cells into two classes i.e cells with MAC and cells without MAC. This study demonstrates that the presence of MAC cells in conventional microscopic sputum cytology images can be identified using image processing techniques and it can have some significance in the early detection of lung cancer. � 2016 IEEE.
Study of malignancy associated changes in sputum images as an indicator of lung cancer
(Institute of Electrical and Electronics Engineers Inc., 2017) Sudheesh, R.K.; Rajan, J.; Veena, V.S.; Sujathan, K.
Lung cancer is one among the major causes of cancer related deaths. Fortunately, an early stage diagnosis can increase the survival rates of the patients. Sputum cytology is one of the easiest and cost-effective method for lung cancer diagnosis. Chances of misdiagnosis and sampling error related to sputum cytology led to the concept of malignancy associated changes. Malignancy associated changes (MAC) are the subtle changes that happens to the normal appearing cells near or distant from the malignant cells. Literature suggests that these changes can be used as an indicator for lung cancer rather than using malignant cells which are very less in number compared to the normal appearing cells in sputum cytology images. The proposed work is intended to detect cells with MAC from sputum smear images. Analysis of nuclei texture features of sputum cell nuclei using Gray Level Co-occurrence Matrix and Gray Level Run Length Matrix from both normal and cancer patients revealed that both type of cells could be differentiated. Among 110 texture features calculated for each nuclei, a set of 35 features which clearly distinguishes normal cells and normal appearing cells were chosen. Support Vector Machine (SVM) classifier is used to classify the cells into two classes i.e cells with MAC and cells without MAC. This study demonstrates that the presence of MAC cells in conventional microscopic sputum cytology images can be identified using image processing techniques and it can have some significance in the early detection of lung cancer. Â© 2016 IEEE.