Biblio

The panic among medical control, information, and device administrators is due to surmounting number of high-profile attacks on healthcare facilities. This hostile situation is going to lead the health informatics industry to cloud-hoarding of medical data, control flows, and site governance. While different healthcare enterprises opt for cloud-based solutions, it is a matter of time when fog computing environment are formed. Because of major gaps in reported techniques for fog security administration for health data i.e. absence of an overarching certification authority (CA), the security provisioning is one of the the issue that we address in this paper. We propose a security provisioning model (AZSPM) for medical devices in fog environments. We propose that the AZSPM can be build by using atomic security components that are dynamically composed. The verification of authenticity of the atomic components, for trust sake, is performed by calculating the processor clock cycles from service execution at the resident hardware platform. This verification is performed in the fully sand boxed environment. The results of the execution cycles are matched with the service specifications from the manufacturer before forwarding the mobile services to the healthcare cloud-lets. The proposed model is completely novel in the fog computing environments. We aim at building the prototype based on this model in a healthcare information system environment.

Being an era of fast internet-based application environment, large volumes of relational data are being outsourced for business purposes. Therefore, ownership and digital rights protection has become one of the greatest challenges and among the most critical issues. This paper presents a novel fingerprinting technique to protect ownership rights of non-numeric digital data on basis of pattern generation and row association schemes. Firstly, fingerprint sequence is formulated by using secret key and buyer's Unique ID. With the chunks of these sequences and by applying the Fibonacci series, we select some rows. The selected rows are candidates of fingerprinting. The primary key of selected row is protected using RSA encryption; after which a pattern is designed by randomly choosing the values of different attributes of datasets. The encryption of primary key leads to develop an association between original and fake pattern; creating an ease in fingerprint detection. Fingerprint detection algorithm first finds the fake rows and then extracts the fingerprint sequence from the fake attributes, hence identifying the traitor. Some most important features of the proposed approach is to overcome major weaknesses such as error tolerance, integrity and accuracy in previously proposed fingerprinting techniques. The results show that technique is efficient and robust against several malicious attacks.