G. Kavitha ¹, R. Priya ¹

Affiliations
1 School of Computing Sciences, Vels University, Pallavaram, Chennai - 600117, Tamil Nadu, IN

Abstract

Objectives: Cloud computing is the sort of computing process that shares the data and its resources in the Internet. Data Leakage is the main challenge prevails in the federated cloud storage service. This challenge is overcome by proposing an efficient searchable encryption system. Methodology: An improved searchable encryption is designed to apportion the data among the multi-users. An efficient key management is the required phase of the encryption schemes. Owing to adversaries, a promising approach using conglomerate keys invents as a better searchable encryption schemes in multiowner applications. Findings: By reducing the count of trapdoor function to cloud data from the multi-user, an effective search over the encrypted data is performed. In compare to existing models, tree based approach and one to one function, the proposed, improved SE-CK works efficient in terms of lessened delegation ratio and conglomerate keys. Applications/ Improvements: This method can also be extended to the hybrid architecture like Indexing schemes, Re-encryption schemes etc.

Keywords

Data Sharing, Data Leakage and Multi-Owner Applications, Federate Cloud Storage, Searchable Encryption, Trapdoor Functions

Full Text

   

R. Priya ¹, R. Padmajavalli ²

Affiliations
1 Research and Development Centre, Bharathiar University, Coimbatore - 641046, Tamil Nadu, IN
2 Department of Computer Applications, Bhaktavatsalam Memorial College for Women, Chennai - 600080, Tamil Nadu, IN

Abstract

Diagnosis of diseases is a difficult work that has to do in accurate manner. Text mining deals a great job in this field. A huge mass of data is available in biomedical field, using this data we can diagnosis many diseases by text mining techniques in efficient manner. Text mining methods are used to retrieve useful knowledge from large data. Objective: The aim of this paper is to review several text mining methods used in biomedical field. This survey is helpful to select a best text mining method for biomedical data. Methods/Analysis: In this paper, classification method is used to study the biomedical text mining for diagnosing diseases. In the field of biomedical, classification can be done on the basis of patient disease pattern to separate the patients into high risk or low risk The classification techniques have two methods they are Binary contains two classes and multilevel contains more than two classes. Classification method is widely used in biomedical text mining. In this paper different classification techniques can be applied to categorize the text they are SVM (Support Vector Machine) NN (Neural Network), K-NN (K-Nearest Neighbor), Bayesian Method and DT (Decision Tree). Findings: In this paper, different classification techniques were surveyed and their merits and limitations have been discussed. The various classification techniques were applied in medical data where useful patterns and knowledge were extracted. The important task is that to select the suitable data and classification method for disease diagnosis. The objective of this survey is that how the classification methods are applied in biomedical application and to select which method is suitable and efficient for diagnosis of a particular disease. Novelty/Improvement: The main advantage of the survey is that it can be applied to any kind of dataset, it is a description dataset or not. For future improvement, we will implement our proposed methodology on using some major chest diseases datasets and measured performance in terms of training time and accurate diagnosis.

Keywords

Biomedical Text Mining, Classification, Concept Linkage, IE (Information Extraction), Topic Tracking.

Full Text