A comparative study of cloud services use by prospective IT professionals in five countries

Individuals and organizations utilise the cloud technology and its services in various ways. Cloud-based services are becoming increasingly popular, while there is no adequate knowledge offered for their secure use in the education for future IT professionals. It is important to understand how security and privacy issues are perceived and handled by male/female users and IT professionals of different cultures. The authors aim at presenting and scrutinizing information about cloud services’ use by prospective IT professionals in five countries, namely China, Finland, Greece, Nepal, and the UK. In particular the authors, wanting to find out what are the future IT professionals’ conceptualisations and awareness, collected data from male and female IT students in higher education, who use (or not) cloud services. The authors further illustrate the research findings by proceeding to a comparative analysis considering different perspectives such as: gender, education background, national culture (values and culture), and IT-related knowledge. The final research outcomes reveal attention-grabbing information for future IT professionals’ skills, knowledge, and digital competencies. For the IT professionals and software quality engineering communities the latter comprise a body of realistic knowledge, worthy of note when designing curricula for security technology by accommodating practical and accessible solutions (e.g., cryptography-based cloud security) for developing and enhancing the IT professionals’ role

ABAKA : a novel attribute-based k-anonymous collaborative solution for LBSs

The increasing use of mobile devices, along with advances in telecommunication systems, increased the popularity of Location-Based Services (LBSs). In LBSs, users share their exact location with a potentially untrusted Location-Based Service Provider (LBSP). In such a scenario, user privacy becomes a major con- cern: the knowledge about user location may lead to her identification as well as a continuous tracing of her position. Researchers proposed several approaches to preserve users’ location privacy. They also showed that hiding the location of an LBS user is not enough to guarantee her privacy, i.e., user’s pro- file attributes or background knowledge of an attacker may reveal the user’s identity. In this paper we propose ABAKA, a novel collaborative approach that provides identity privacy for LBS users considering users’ profile attributes. In particular, our solution guarantees p -sensitive k -anonymity for the user that sends an LBS request to the LBSP. ABAKA computes a cloaked area by collaborative multi-hop forwarding of the LBS query, and using Ciphertext-Policy Attribute-Based Encryption (CP-ABE). We ran a thorough set of experiments to evaluate our solution: the results confirm the feasibility and efficiency of our proposal

Using mobile technology to engage sexual and gender minorities in clinical research.

IntroductionHistorical and current stigmatizing and discriminatory experiences drive sexual and gender minority (SGM) people away from health care and clinical research. Being medically underserved, they face numerous disparities that make them vulnerable to poor health outcomes. Effective methods to engage and recruit SGM people into clinical research studies are needed.ObjectivesTo promote health equity and understand SGM health needs, we sought to design an online, national, longitudinal cohort study entitled The PRIDE (Population Research in Identity and Disparities for Equality) Study that enabled SGM people to safely participate, provide demographic and health data, and generate SGM health-related research ideas.MethodsWe developed an iPhone mobile application ("app") to engage and recruit SGM people to The PRIDE Study-Phase 1. Participants completed demographic and health surveys and joined in asynchronous discussions about SGM health-related topics important to them for future study.ResultsThe PRIDE Study-Phase 1 consented 18,099 participants. Of them, 16,394 provided data. More than 98% identified as a sexual minority, and more than 15% identified as a gender minority. The sample was diverse in terms of sexual orientation, gender identity, age, race, ethnicity, geographic location, education, and individual income. Participants completed 24,022 surveys, provided 3,544 health topics important to them, and cast 60,522 votes indicating their opinion of a particular health topic.ConclusionsWe developed an iPhone app that recruited SGM adults and collected demographic and health data for a new national online cohort study. Digital engagement features empowered participants to become committed stakeholders in the research development process. We believe this is the first time that a mobile app has been used to specifically engage and recruit large numbers of an underrepresented population for clinical research. Similar approaches may be successful, convenient, and cost-effective at engaging and recruiting other vulnerable populations into clinical research studies

VirtualIdentity : privacy preserving user profiling

User profiling from user generated content (UGC) is a common practice that supports the business models of many social media companies. Existing systems require that the UGC is fully exposed to the module that constructs the user profiles. In this paper we show that it is possible to build user profiles without ever accessing the user's original data, and without exposing the trained machine learning models for user profiling - which are the intellectual property of the company - to the users of the social media site. We present VirtualIdentity, an application that uses secure multi-party cryptographic protocols to detect the age, gender and personality traits of users by classifying their user-generated text and personal pictures with trained support vector machine models in a privacy preserving manner

Big Data Privacy Context: Literature Effects On Secure Informational Assets

This article's objective is the identification of research opportunities in the current big data privacy domain, evaluating literature effects on secure informational assets. Until now, no study has analyzed such relation. Its results can foster science, technologies and businesses. To achieve these objectives, a big data privacy Systematic Literature Review (SLR) is performed on the main scientific peer reviewed journals in Scopus database. Bibliometrics and text mining analysis complement the SLR. This study provides support to big data privacy researchers on: most and least researched themes, research novelty, most cited works and authors, themes evolution through time and many others. In addition, TOPSIS and VIKOR ranks were developed to evaluate literature effects versus informational assets indicators. Secure Internet Servers (SIS) was chosen as decision criteria. Results show that big data privacy literature is strongly focused on computational aspects. However, individuals, societies, organizations and governments face a technological change that has just started to be investigated, with growing concerns on law and regulation aspects. TOPSIS and VIKOR Ranks differed in several positions and the only consistent country between literature and SIS adoption is the United States. Countries in the lowest ranking positions represent future research opportunities.Comment: 21 pages, 9 figure

User-centric distributed solutions for privacy-preserving analytics

How can cryptography empower users with sensitive data to access large-scale computing platforms in a privacy-preserving manner?</jats:p