Cloud computing has pioneered the area of On-demand services. The customer can choose the resources according to the current needs with the facility of incrementing and decrementing the resources in the future. It generally follows a pay-as-you-use model which has proven beneficial for enterprises and individual users alike. Since the services are hosted over the internet, one of the recent concerns that are rising among the users is about the location of their data. Sometimes it is necessary for the data to stay in a particular jurisdiction. Therefore, it may be required for the organization to verify the location of their data from time to time. Here in this paper we propose a mechanism based on remote attestation technology of trusted platform module. Remote attestation technique is used to validate the current location of the data, and the generated result is passed to the user/verifier. The very fact that the trusted platform module is tamper proof provides the basis for the accuracy of the result.

While the use of artificial intelligence (AI) for medical image analysis is gaining wide acceptance, the expertise, time and cost required to generate annotated data in the medical field are significantly high, due to limited availability of both data and expert annotation. Strongly supervised object localization models require data that is exhaustively annotated, meaning all objects of interest in an image are identified. This is difficult to achieve and verify for medical images. We present a method for the transformation of real data to train any Deep Neural Network to solve the above problems. We show the efficacy of this approach on both a weakly supervised localization model and a strongly supervised localization model. For the weakly supervised model, we show that the localization accuracy increases significantly using the generated data. For the strongly supervised model, this approach overcomes the need for exhaustive annotation on real images. In the latter model, we show that the accuracy, when trained with generated images, closely parallels the accuracy when trained with exhaustively annotated real images. The results are demonstrated on images of human urine samples obtained using microscopy.

Abstract-Unified Extensible Firmware Interface (UEFI) is a new specification that defines a software interface between the platform firmware and the operating system. UEFI in the near future will replace the conventional Basic Input-Output System (BIOS). Along with this, Trusted Computing has emerged as a new and challenging research field in the domain of computer security. This asserts the need of Trusted Bootstrapping. Here a new idea of Trusted Bootstrapping using the USB key is presented which involves the scheme of Portable Trusted Platform Module, supported with UEFI technology. It aims to reduce motherboard modification and makes the system less vulnerable to human disruption.

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