Anonymous Authentication for Smartcards

The paper presents an innovative solution in the field of RFID (Radio-Frequency IDentification) smartcard authentication. Currently the smartcards are used for many purposes - e.g. employee identification, library cards, student cards or even identity credentials. Personal identity is revealed to untrustworthy entities every time we use these cards. Such information could later be used without our knowledge and for harmful reasons like shopping pattern scanning or even movement tracking. We present a communication scheme for keeping one’s identity private in this paper. Although our system provides anonymity, it does not allow users to abuse this feature. The system is based on strong cryptographic primitives that provide features never available before. Besides theoretical design of the anonymous authentication scheme and its analysis we also provide implementation results

Privacy-preserving security solution for cloud services

AbstractWe propose a novel privacy-preserving security solution for cloud services. Our solution is based on an efficient non-bilinear group signature scheme providing the anonymous access to cloud services and shared storage servers. The novel solution offers anonymous authenticationfor registered users. Thus, users' personal attributes (age, valid registration, successful payment) can be proven without revealing users' identity, and users can use cloud services without any threat of profiling their behavior. However, if a user breaks provider's rules, his access right is revoked. Our solution provides anonymous access, unlinkability and the confidentiality of transmitted data. We implement our solution as a proof of concept applicationand present the experimental results. Further, we analyzecurrent privacy preserving solutions for cloud services and group signature schemes as basic parts of privacy enhancing solutions in cloud services. We compare the performance of our solution with the related solutionsand schemes

Assessing urban methane emissions using column-observing portable Fourier transform infrared (FTIR) spectrometers and a novel Bayesian inversion framework

Cities represent a large and concentrated portion of global greenhouse gas emissions, including methane. Quantifying methane emissions from urban areas is difficult, and inventories made using bottom-up accounting methods often differ greatly from top-down estimates generated from atmospheric observations. Emissions from leaks in natural gas infrastructure are difficult to predict and are therefore poorly constrained in bottom-up inventories. Natural gas infrastructure leaks and emissions from end uses can be spread throughout the city, and this diffuse source can represent a significant fraction of a city\u27s total emissions. We investigated diffuse methane emissions of the city of Indianapolis, USA, during a field campaign in May 2016. A network of five portable solar-tracking Fourier transform infrared (FTIR) spectrometers was deployed throughout the city. These instruments measure the mole fraction of methane in a total column of air, giving them sensitivity to larger areas of the city than in situ sensors at the surface. We present an innovative inversion method to link these total column concentrations to surface fluxes. This method combines a Lagrangian transport model with a Bayesian inversion framework to estimate surface emissions and their uncertainties, together with determining the concentrations of methane in the air flowing into the city. Variations exceeding 10 ppb were observed in the inflowing air on a typical day, which is somewhat larger than the enhancements due to urban emissions (<5 ppb downwind of the city). We found diffuse methane emissions of 73(±22) mol s−1, which is about 50 % of the urban total and 68 % higher than estimated from bottom-up methods, although it is somewhat smaller than estimates from studies using tower and aircraft observations. The measurement and model techniques developed here address many of the challenges present when quantifying urban greenhouse gas emissions and will help in the design of future measurement schemes in other cities

A non-invasive electricity measurement within the smart grid landscape: Arduino-based visualization platform for IoT

Measurements of consumption whether we talk about gas, water or electricity are becoming more digitized every day. Distribution companies want to be able to read the state of a meter without the need of the physical presence of the employee in the place of interest. This leads to development and implementation of smart grids using smart (embedded) devices as a part of the Internet of Things (IoT) or Industry 4.0 vision. In the first part of our paper, we discuss the evolution of smart grids and summarize the wireless technologies used nowadays for data transmissions from the sensors towards the remote nodes, where the data processing takes place. Afterwards, we propose an IoT platform for non-invasive measurement of electric current. Our design is based on current transformer measurements and works well for values of alternating current up to 30 A with extension to 100 A which significantly increases the impact for smart grid use-cases. Processing of measured data is done within the utilized Arduino board. Current readings can be displayed on attached LCD panel or sent to developed web server application which takes a part of our proposed solution. To verify the stability and accuracy of our newly designed platform, we conducted measurements for current transformer linearity and measurements on off the shelf meter from Meazon as also on AC power source from Keysight company. Maximum measured deviation of our system was 2.35 % and average calculated deviation was 1.5 %. Maximum linearity deviation of the current transformer during measurements was 1.34 %. © 2017 IEEE

A non-invasive electricity measurement within the smart grid landscape: Arduino-based visualization platform for IoT

Measurements of consumption whether we talk about gas, water or electricity are becoming more digitized every day. Distribution companies want to be able to read the state of a meter without the need of the physical presence of the employee in the place of interest. This leads to development and implementation of smart grids using smart (embedded) devices as a part of the Internet of Things (IoT) or Industry 4.0 vision. In the first part of our paper, we discuss the evolution of smart grids and summarize the wireless technologies used nowadays for data transmissions from the sensors towards the remote nodes, where the data processing takes place. Afterwards, we propose an IoT platform for non-invasive measurement of electric current. Our design is based on current transformer measurements and works well for values of alternating current up to 30 A with extension to 100 A which significantly increases the impact for smart grid use-cases. Processing of measured data is done within the utilized Arduino board. Current readings can be displayed on attached LCD panel or sent to developed web server application which takes a part of our proposed solution. To verify the stability and accuracy of our newly designed platform, we conducted measurements for current transformer linearity and measurements on off the shelf meter from Meazon as also on AC power source from Keysight company. Maximum measured deviation of our system was 2.35 % and average calculated deviation was 1.5 %. Maximum linearity deviation of the current transformer during measurements was 1.34 %. © 2017 IEEE