A Cloud-based RFID Authentication Protocol with Insecure Communication Channels

© 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Radio Frequency Identification (RFID) has becomea widespread technology to automatically identify objects and withthe development of cloud computing, cloud-based RFID systemsattract more research these days. Several cloud-based RFIDauthentication protocols have been proposed to address privacyand security properties in the environment where the cloudprovider is untrusted therefore the tag’s data are encrypted andanonymously stored in the cloud database. However, most of thecloud-based RFID authentication protocols assume securecommunication channels between the reader and the cloud server.To protect data transmission between the reader and the cloudserver without any help from a third party, this paper proposes acloud-based RFID authentication protocol with insecurecommunication channels (cloud-RAPIC) between the reader and the cloud server. The cloud-RAPIC protocol preserves tag privacyeven when the tag does not update its identification. The cloudRAPIC protocol has been analyzed using the UPriv model andAVISPA verification tool which have proved that the protocolpreserves tag privacy and protects data secrecy

Smart cards: State-of-the-art to future directions

The evolution of smart card technology provides an interesting case study of the relationship and interactions between security and business requirements. This paper maps out the milestones for smart card technology, discussing at each step the opportunities and challenges. The paper reviews recently proposed innovative ownership/management models and the security challenges associated with them. The paper concludes with a discussion of possible future directions for the technology, and the challenges these present

Arguing satisfaction of security requirements

This chapter presents a process for security requirements elicitation and analysis, based around the construction of a satisfaction argument for the security of a system. The process starts with the enumeration of security goals based on assets in the system, then uses these goals to derive security requirements in the form of constraints. Next, a satisfaction argument for the system is constructed, using a problem-centered representation, a formal proof to analyze properties that can be demonstrated, and structured informal argumentation of the assumptions exposed during construction of the argument. Constructing the satisfaction argument can expose missing and inconsistent assumptions about system context and behavior that effect security, and a completed argument provides assurances that a system can respect its security requirements

Product specification documentation standard and Data Item Descriptions (DID). Volume of the information system life-cycle and documentation standards, volume 3

This is the third of five volumes on Information System Life-Cycle and Documentation Standards which present a well organized, easily used standard for providing technical information needed for developing information systems, components, and related processes. This volume states the Software Management and Assurance Program documentation standard for a product specification document and for data item descriptions. The framework can be applied to any NASA information system, software, hardware, operational procedures components, and related processes

High-Throughput Classification of Radiographs Using Deep Convolutional Neural Networks.

The study aimed to determine if computer vision techniques rooted in deep learning can use a small set of radiographs to perform clinically relevant image classification with high fidelity. One thousand eight hundred eighty-five chest radiographs on 909 patients obtained between January 2013 and July 2015 at our institution were retrieved and anonymized. The source images were manually annotated as frontal or lateral and randomly divided into training, validation, and test sets. Training and validation sets were augmented to over 150,000 images using standard image manipulations. We then pre-trained a series of deep convolutional networks based on the open-source GoogLeNet with various transformations of the open-source ImageNet (non-radiology) images. These trained networks were then fine-tuned using the original and augmented radiology images. The model with highest validation accuracy was applied to our institutional test set and a publicly available set. Accuracy was assessed by using the Youden Index to set a binary cutoff for frontal or lateral classification. This retrospective study was IRB approved prior to initiation. A network pre-trained on 1.2 million greyscale ImageNet images and fine-tuned on augmented radiographs was chosen. The binary classification method correctly classified 100 % (95 % CI 99.73-100 %) of both our test set and the publicly available images. Classification was rapid, at 38 images per second. A deep convolutional neural network created using non-radiological images, and an augmented set of radiographs is effective in highly accurate classification of chest radiograph view type and is a feasible, rapid method for high-throughput annotation

Why TaxMe Makes Taxpayers Happy?

TaxMe-Online is the online tax declaration system of the Canton of Bern in Switzerland, where each of the 26 Cantons has its own fiscal regime and taxation system. In 2008 it was used by almost 26 percent of the Canton of Bern taxpayers (42% used the TaxMe-CD or other software, and the last third chose the paper forms). The TaxMe portal furthermore gives taxpayer access to their fiscal data (taxation status, amounts paid, etc.) and allows them to send electronic vouchers. TaxMe-Online does not require any preliminary registration as the taxpayers receive their user ID at the same time as the tax declaration forms, and when they log in with their identification data, their identity data (name, address, etc.) are already available. Users do however have to sign a paper-based validation declaration: until they have done so, their online tax declaration is not considered as finalized. The tax administration does not have the right to access taxpayers' data until the receipt of this validation declaration. TaxMe-Online is built on open source components and solutions; data are coded before being sent electronically (Secure Socket Layer). 33% of the TaxMe-Online users say they are “very happy” with this way of filling in their tax declaration, but amongst citizens using a similar solution on CD-ROM or the paper-based declaration, only 18% say they are very happy. This paper tries to find out why the online solution scores much higher than other tax declaration systems. It comprises three main parts (i) the development of an assessment model; (ii) a description of the system and its functionalities; and (iii) an analysis of user acceptance. We investigated the point of view of the TaxMe-Online users on an empirical basis, most notably by analysing secondary sources such as surveys realized by the fiscal administration of the Canton of Bern and newspaper articles, and by conducting interviews with various stakeholders.Taxation; case study; usability; portal; data exchange; open source; user acceptance