Biblio

Healthcare experts and researchers have been promoting the need for IoT-based remote health monitoring systems that take care of the health of elderly people. However, such systems may generate large amounts of data, which makes the security and privacy of such data to become imperative. This paper studies the security and privacy concerns of the existing Healthcare Monitoring System (HMS) and proposes a reference architecture (security integration framework) for managing IoT-based healthcare monitoring systems that ensures security, privacy, and reliable service delivery for patients and elderly people to reduce and avoid health related risks. Our proposed framework will be in the form of state-of-the-art Security Platform, for HMS, using the emerging Software Defined Network (SDN) networking paradigm. Our proposed integration framework eliminates the dependency on specific Software or vendor for different security systems, and allows for the benefits from the functional and secure applications, and services provided by the SDN platform.

Recent breakthrough in neural network has led to the birth of Convolutional neural network (CNN) which has been found to be very efficient especially in the areas of image recognition and classification. This success is traceable to the availability of large datasets and its capability to learn salient and complex data features which subsequently produce a reusable output model (Fθ). The Fθ are often made available (e.g. on cloud as-a-service) for others (client) to train their data or do transfer learning, however, an adversary can perpetrate a model inversion attack on the model Fθ to recover training data, hence compromising the sensitivity of the model buildup data. This is possible because CNN as a variant of deep neural network does memorize most of its training data during learning. Consequently, this has pose a privacy concern especially when a medical or financial data are used as model buildup data. Existing researches that proffers privacy preserving approach however suffer from significant accuracy degradation and this has left privacy preserving model on a theoretical desk. In this paper, we proposed an ϵ-tuple differential privacy approach that is based on neuron impact factor estimation to preserve privacy of CNN model without significant accuracy degradation. We experiment our approach on two large datasets and the result shows no significant accuracy degradation.

The expeditious expansion of the networking technologies have remarkably driven the usage of the distributedcomputing as well as services, such as task offloading to the cloud. However, security and privacy concerns are restricting the implementations of cloud computing because of the threats from both outsiders and insiders. The primary alternative of protecting users' data is developing a Fully Homomorphic Encryption (FHE) scheme, which can cover both data protections and data processing in the cloud. Despite many previous attempts addressing this approach, none of the proposed work can simultaneously satisfy two requirements that include the non-noise accuracy and an efficiency execution. This paper focuses on the issue of FHE design and proposes a novel FHE scheme, which is called Optimal Fully Homomorphic Encryption (O-FHE). Our approach utilizes the properties of the Kronecker Product (KP) and designs a mechanism of achieving FHE, which consider both accuracy and efficiency. We have assessed our scheme in both theoretical proofing and experimental evaluations with the confirmed and exceptional results.

Information Technology experts cite security and privacy concerns as the major challenges in the adoption of cloud computing. On Platform-as-a-Service (PaaS) clouds, customers are faced with challenges of selecting service providers and evaluating security implementations based on their security needs and requirements. This study aims to enable cloud customers the ability to quantify their security requirements in order to identify critical areas in PaaS cloud architectures were security provisions offered by CSPs could be assessed. With the use of an adaptive security mapping matrix, the study uses a quantitative approach to presents findings of numeric data that shows critical architectures within the PaaS environment where security can be evaluated and security controls assessed to meet these security requirements. The matrix can be adapted across different types of PaaS cloud models based on individual security requirements and service level objectives identified by PaaS cloud customers.

Cloud Computing delivers the service to the users by having reliable internet connection. In the secure cloud, services are stored and shared by multiple users because of less cost and data maintenance. Sharing the data is the vital intention of cloud data centres. On the other hand, storing the sensitive information is the privacy concern of the cloud. Cloud service provider has to protect the stored client's documents and applications in the cloud by encrypting the data to provide data integrity. Designing proficient document sharing among the group members in the cloud is the difficult task because of group user membership change and conserving document and group user identity confidentiality. To propose the fortified data sharing scheme in secret manner for providing efficient group revocation Advanced Encryption Standard scheme is used. Proposed System contributes efficient group authorization, authentication, confidentiality and access control and document security. To provide more data security Advanced Encryption Standard algorithm is used to encrypt the document. By asserting security and confidentiality in this proficient method securely share the document among the multiple cloud user.
 

The innovations in communication and computing technologies are changing the way we carry-out the tasks in our daily lives. These revolutionary and disrupting technologies are available to the users in various hardware form-factors like Smart Phones, Embedded Appliances, Configurable or Customizable add-on devices, etc. One such technology is Bluetooth [1], which enables the users to communicate and exchange various kinds of information like messages, audio, streaming music and file transfer in a Personal Area Network (PAN). Though it enables the user to carry-out these kinds of tasks without much effort and infrastructure requirements, they inherently bring with them the security and privacy concerns, which need to be addressed at different levels. In this paper, we present an application-layer framework, which provides strong mutual authentication of applications, data confidentiality and data integrity independent of underlying operating system. It can make use of the services of different Cryptographic Service Providers (CSP) on different operating systems and in different programming languages. This framework has been successfully implemented and tested on Android Operating System on one end (using Java language) and MS-Windows 7 Operating System on the other end (using ANSI C language), to prove the framework's reliability/compatibility across OS, Programming Language and CSP. This framework also satisfies the three essential requirements of Security, i.e. Confidentiality, Integrity and Availability, as per the NIST Guide to Bluetooth Security specification and enables the developers to suitably adapt it for different kinds of applications based on Bluetooth Technology.