Security and Privacy Issues of Big Data

This chapter revises the most important aspects in how computing infrastructures should be configured and intelligently managed to fulfill the most notably security aspects required by Big Data applications. One of them is privacy. It is a pertinent aspect to be addressed because users share more and more personal data and content through their devices and computers to social networks and public clouds. So, a secure framework to social networks is a very hot topic research. This last topic is addressed in one of the two sections of the current chapter with case studies. In addition, the traditional mechanisms to support security such as firewalls and demilitarized zones are not suitable to be applied in computing systems to support Big Data. SDN is an emergent management solution that could become a convenient mechanism to implement security in Big Data systems, as we show through a second case study at the end of the chapter. This also discusses current relevant work and identifies open issues.Comment: In book Handbook of Research on Trends and Future Directions in Big Data and Web Intelligence, IGI Global, 201

User's Privacy in Recommendation Systems Applying Online Social Network Data, A Survey and Taxonomy

Recommender systems have become an integral part of many social networks and extract knowledge from a user's personal and sensitive data both explicitly, with the user's knowledge, and implicitly. This trend has created major privacy concerns as users are mostly unaware of what data and how much data is being used and how securely it is used. In this context, several works have been done to address privacy concerns for usage in online social network data and by recommender systems. This paper surveys the main privacy concerns, measurements and privacy-preserving techniques used in large-scale online social networks and recommender systems. It is based on historical works on security, privacy-preserving, statistical modeling, and datasets to provide an overview of the technical difficulties and problems associated with privacy preserving in online social networks.Comment: 26 pages, IET book chapter on big data recommender system

An evaluation of professional networks, co-ordination, cooperation and collaboration in the West Midlands Paediatric Palliative Care Network

Introduction: This is a report on Strand 3 of the Big Study, which studied the West Midlands Paediatric Palliative Care Network. The Big Study was funded by The Big Lottery Fund and Strand 3 of the Big Study was researched by the Centre for Nursing and Healthcare Research in the School of Health and Social Care at the University of Greenwich. 1.1 Background: The West Midlands Paediatric Palliative Care Network began as an interest group which started in the year 2000, with 6 to 10 members and grew. At one stage it was allied to the Birmingham Cancer Network and funded by the NHS Strategic Health Authority and at this stage it became more representative of services and West Midlands geography. It has existed in its current format, as a voluntary clinical network to promote paediatric palliative care and share best practice since 2009. The membership is wide and inclusive which means 30 to 40 people may attend the meetings which are held on a bimonthly basis and are hosted and supported charitably. Subgroups are now used to manage work in specific areas e.g. transition or clinical standards. There are links to other related networks with reciprocal membership and informal links to NHS commissioners who may seek advice. 1.2 Scope: This strand of the Big Study focused on the West Midlands Paediatric Palliative Care Network. The geographical area of the West Midlands Paediatric Palliative Care Network includes Birmingham, Coventry, The Black Country, Herefordshire, Shropshire, Solihull, Staffordshire, Stoke-on-Trent, Telford and Wrekin, Warwickshire and Worcestershire. All members of the WMPCCN and the organisations they represent were included in the study. Both NHS and non-NHS organisations offering clinical services to any children requiring palliative care were represented. Excluded from this study was the detailed examination of any of the other networks, e.g. children’s speciality networks or networks covering smaller geographical areas, to which members belonged. 1.3 Report: This report will present the results of an analysis of the responses to an online questionnaire and Social Network data from semi structured telephone interviews. This data was collected during the period February to June 2012. The approach included analysing the online survey data in order to understand the benefits and constraints of the network for individual members and Social Network Analysis of data derived from telephone interviews to explore the flow of knowledge, communication and information within the network. This report will consist of 3 different sections, with Section 1 focusing on children’s palliative care policy, the development of clinical networks and social network analysis concepts. Section 2 will focus on the research design and methods. Section 3 presents the results of the study and the final section provides a summary and conclusions of the analysis

Social Networks, Big Data and Transport Planning

[EN] The characteristics of people who are related or tied to each individual affects her activity-travel behavior. That influence is especially associated to social and recreational activities, which are increasingly important. Collecting high quality data from those social networks is very difficult using traditional travel surveys, because respondents are asked about their general social life, which is most demanding to remember that specific facts. On the other hand, currently there are different potential sources of transport data, which is characterized by the huge amount of information available, the velocity with it is obtained and the variety of format in which is presented. This sort of information is commonly known as Big Data. To use this data on Transport Planning application is a challenge, which require employing complex data mining techniques. In this paper, we identify potential sources of social network related big data that can be used in Transport Planning, discussing their advantages and limitations. Then, a review of current applications in Transport Planning is presented. Finally, some future prospects of using social network related big data that are included in the MINERVA project are highlighted.Cost Action TU1305 Social Networks and Travel Behaviour, MINECORuiz Sánchez, T.; Mars Aicart, MDL.; Arroyo-López, MR.; Serna, A. (2016). Social Networks, Big Data and Transport Planning. Transportation Research Procedia. 18:446-452. doi:10.1016/j.trpro.2017.01.122S4464521