Cloud computing model is very exciting model especially for business peoples. Many business peoples are getting attracted towards cloud computing model because of the features easy to manage, device independent, location independent. But this cloud models comes with many security issues. A business person keeps crucial information on cloud, so security of data is crucial issue as probability of hacking and unauthorised access is there. Also availability is a major concern on cloud. This paper, discusses the file distribution and SHA-1 technique. When file is distributed then data is also segregated into many servers. So here the need of data security arises. Every block of file contains its own hash code, using hash code which will enhance user authentication process; only authorized person can access the data. Here, the data is encrypted using advanced encryption standard, so data is successfully and securely stored on cloud. Third party auditor is used for public auditing. This paper discusses the handling of some security issues like Fast error localization, data integrity, data security. The proposed design allows users to audit the data with lightweight communication and computation cost. Analysis shows that proposed system is highly efficient against malicious data modification attack and server colluding attack. Performance and extensive security analysis shows that proposed systems are provably secure and highly efficient.
Vast amounts of clinical and biomedical research data are produced daily. These data can help enable data driven healthcare through novel biomedical discoveries, improved diagnostics processes, epidemiology, and education. However, finding, and gaining access to these data and relevant metadata that are necessary to achieve these goals remains a challenge. Furthermore, data management and enabling widespread, albeit controlled, use poses a major challenge for data producers. These data sources are often geographically distributed, with diverse characteristics, and are controlled by a host of logistical and legal factors that require appropriate governance and access control guarantees. To overcome these obstacles, a set of guiding principles under the term FAIR has been previously introduced. The primary desirable dataset properties are thus that the data should be Findable, Accessible, Interoperable, and Reusable (FAIR). In this paper, we introduce and describe an abstract framework that models these ideal goals, and could be a step toward supporting data driven research. We also develop a system instantiated on our framework called the Data integration and indexing System (DiiS). The system provides an integration model for making healthcare data available on a global scale. Our research work describes the challenges inhibiting data producers, data stewards, and data brokers in achieving FAIR goals for sharing biomedical data. We attempt to address some of the key challenges through the proposed system. We evaluated our framework using the software architecture testing technique and also looked at how different challenges in data integration are addressed by our system. Our evaluation shows that the DiiS framework is a user friendly data integration system that would greatly contribute to biomedical research.
Teaching files are widely used by radiologists in the diagnostic process and for student education. Most hospitals maintain an active collection of teaching files for internal purposes, but many teaching files are also publicly available online, some linked to secondary sources. However, public sources offer very limited (and ad-hoc) search capabilities. Based on the previous work on data integration and text-based search, the authors extended their Integrated Radiology Image Search (IRIS 1.1) engine with a new medical ontology, SNOMED CT, and the ICD10 dictionary. IRIS 1.1 integrates public data sources and applies query expansion with exact and partial matches to find relevant teaching files. Using a set of 28 representative queries from multiple sources, the search engine finds more relevant teaching cases versus other publicly available search engines.
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