Data security issues in cloud scenarios

The amount of data created, stored, and processed has enormously increased in the last years. Today, millions of devices are connected to the Internet and generate a huge amount of (personal) data that need to be stored and processed using scalable, efficient, and reliable computing infrastructures. Cloud computing technology can be used to respond to these needs. Although cloud computing brings many benefits to users and companies, security concerns about the cloud still represent the major impediment for its wide adoption. We briefly survey the main challenges related to the storage and processing of data in the cloud. In particular, we focus on the problem of protecting data in storage, supporting fine-grained access, selectively sharing data, protecting query privacy, and verifying the integrity of computations

A Novel Gesture-based CAPTCHA Design for Smart Devices

CAPTCHAs have been widely used in Web applications to prevent service abuse. With the evolution of computing environment from desktop computing to ubiquitous computing, more and more users are accessing Web applications on smart devices where touch based interactions are dominant. However, the majority of CAPTCHAs are designed for use on computers and laptops which do not reflect the shift of interaction style very well. In this paper, we propose a novel CAPTCHA design to utilise the convenience of touch interface while retaining the needed security. This is achieved through using a hybrid challenge to take advantages of human’s cognitive abilities. A prototype is also developed and found to be more user friendly than conventional CAPTCHAs in the preliminary user acceptance test

The state of peer-to-peer network simulators

Networking research often relies on simulation in order to test and evaluate new ideas. An important requirement of this process is that results must be reproducible so that other researchers can replicate, validate and extend existing work. We look at the landscape of simulators for research in peer-to-peer (P2P) networks by conducting a survey of a combined total of over 280 papers from before and after 2007 (the year of the last survey in this area), and comment on the large quantity of research using bespoke, closed-source simulators. We propose a set of criteria that P2P simulators should meet, and poll the P2P research community for their agreement. We aim to drive the community towards performing their experiments on simulators that allow for others to validate their results

Technical Privacy Metrics: a Systematic Survey

The file attached to this record is the author's final peer reviewed versionThe goal of privacy metrics is to measure the degree of privacy enjoyed by users in a system and the amount of protection offered by privacy-enhancing technologies. In this way, privacy metrics contribute to improving user privacy in the digital world. The diversity and complexity of privacy metrics in the literature makes an informed choice of metrics challenging. As a result, instead of using existing metrics, new metrics are proposed frequently, and privacy studies are often incomparable. In this survey we alleviate these problems by structuring the landscape of privacy metrics. To this end, we explain and discuss a selection of over eighty privacy metrics and introduce categorizations based on the aspect of privacy they measure, their required inputs, and the type of data that needs protection. In addition, we present a method on how to choose privacy metrics based on nine questions that help identify the right privacy metrics for a given scenario, and highlight topics where additional work on privacy metrics is needed. Our survey spans multiple privacy domains and can be understood as a general framework for privacy measurement

Privacy in the Smart City - Applications, Technologies, Challenges and Solutions

Many modern cities strive to integrate information technology into every aspect of city life to create so-called smart cities. Smart cities rely on a large number of application areas and technologies to realize complex interactions between citizens, third parties, and city departments. This overwhelming complexity is one reason why holistic privacy protection only rarely enters the picture. A lack of privacy can result in discrimination and social sorting, creating a fundamentally unequal society. To prevent this, we believe that a better understanding of smart cities and their privacy implications is needed. We therefore systematize the application areas, enabling technologies, privacy types, attackers and data sources for the attacks, giving structure to the fuzzy term “smart city”. Based on our taxonomies, we describe existing privacy-enhancing technologies, review the state of the art in real cities around the world, and discuss promising future research directions. Our survey can serve as a reference guide, contributing to the development of privacy-friendly smart cities

Offline EEG-based driver drowsiness estimation using enhanced batch-mode active learning (EBMAL) for regression

© 2016 IEEE. There are many important regression problems in real-world brain-computer interface (BCI) applications, e.g., driver drowsiness estimation from EEG signals. This paper considers offline analysis: given a pool of unlabeled EEG epochs recorded during driving, how do we optimally select a small number of them to label so that an accurate regression model can be built from them to label the rest? Active learning is a promising solution to this problem, but interestingly, to our best knowledge, it has not been used for regression problems in BCI so far. This paper proposes a novel enhanced batch-mode active learning (EBMAL) approach for regression, which improves upon a baseline active learning algorithm by increasing the reliability, representativeness and diversity of the selected samples to achieve better regression performance. We validate its effectiveness using driver drowsiness estimation from EEG signals. However, EBMAL is a general approach that can also be applied to many other offline regression problems beyond BCI