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

Big Data Security Issues in Three Perspectives: A Review

Big data is a term that is used to describe data that is high volume, high velocity, and/or high variety; requires new technologies and techniques to capture, store, and analyze it; and is used to enhance decision making, provide insight and discovery, and support and optimize processes. With regard to the definition of big data, IBM Company uses volume, velocity, variety, value and veracity as 5Vs to summarize the concept of big data.  There are different types of big data, for example, structured, semi-structured and un-structured data. The contents of big data can be text data, audio data, video data and still image and it indicates that the big data may have diverse data types as well as data qualities. Big data has variety of sources such as healthcare center, commercial system, industries, social media, telecommunication, transportation, sensor machines and others. In this paper, I reviewed three the most security challenging perspectives and I studied lack of concentrations in these areas by most research works. To confirm security in the big data platforms, it is critical to ascertain the data rendering points and their security techniques to safeguard the data in this pacing digital world. Then I envisage directions for the future research. In this paper, I have reviewed the big data sources and its security issues in the three directions such as data at rest, data at communication and data in process/use. Keywords: Big Data, Big Data Security, Big Data source, Attribute based encryption, storage path, Transport layer security, secure shell, Cloud service Provider DOI: 10.7176/CEIS/12-3-01 Publication date: November 30th 202

Contents

We discuss the current minimisation strategies adopted by research projects involving the determination of parton distribution functions (PDFs) and fragmentation functions (FFs) through the training of neural networks. We present a short overview of a proton PDF determination obtained using the covariance matrix adaptation evolution strategy (CMA-ES) optimisation algorithm. We perform comparisons between the CMA-ES and the standard nodal genetic algorithm (NGA) adopted by the NNPDF collaboration

Performance Degradation and Cost Impact Evaluation of Privacy Preserving Mechanisms in Big Data Systems

Big Data is an emerging area and concerns managing datasets whose size is beyond commonly used software tools ability to capture, process, and perform analyses in a timely way. The Big Data software market is growing at 32% compound annual rate, almost four times more than the whole ICT market, and the quantity of data to be analyzed is expected to double every two years. Security and privacy are becoming very urgent Big Data aspects that need to be tackled. Indeed, users share more and more personal data and user-generated content through their mobile devices and computers to social networks and cloud services, losing data and content control with a serious impact on their own privacy. Privacy is one area that had a serious debate recently, and many governments require data providers and companies to protect users’ sensitive data. To mitigate these problems, many solutions have been developed to provide data privacy but, unfortunately, they introduce some computational overhead when data is processed. The goal of this paper is to quantitatively evaluate the performance and cost impact of multiple privacy protection mechanisms. A real industry case study concerning tax fraud detection has been considered. Many experiments have been performed to analyze the performance degradation and additional cost (required to provide a given service level) for running applications in a cloud system

Successfully Implementing Digital Health to Ensure Future Global Health Security During Pandemics A Consensus Statement

IMPORTANCE COVID-19 has highlighted widespread chronic underinvestment in digital health that hampered public health responses to the pandemic. Recognizing this, the Riyadh Declaration on Digital Health, formulated by an international interdisciplinary team of medical, academic, and industry experts at the Riyadh Global Digital Health Summit in August 2020, provided a set of digital health recommendations for the global health community to address the challenges of current and future pandemics. However, guidance is needed on how to implement these recommendations in practice. OBJECTIVE To develop guidance for stakeholders on how best to deploy digital health and data and support public health in an integrated manner to overcome the COVID-19 pandemic and future pandemics. EVIDENCE REVIEW Themes were determined by first reviewing the literature and Riyadh Global Digital Health Summit conference proceedings, with experts independently contributing ideas. Then, 2 rounds of review were conducted until all experts agreed on the themes and main issues arising using a nominal group technique to reach consensus. Prioritization was based on how useful the consensus recommendation might be to a policy maker. FINDINGS A diverse stakeholder group of 13 leaders in the fields of public health, digital health, and health care were engaged to reach a consensus on how to implement digital health recommendations to address the challenges of current and future pandemics. Participants reached a consensus on high-priority issues identified within 5 themes: team, transparency and trust, technology, techquity (the strategic development and deployment of technology in health care and health to achieve health equity), and transformation. Each theme contains concrete points of consensus to guide the local, national, and international adoption of digital health to address challenges of current and future pandemics. CONCLUSIONS AND RELEVANCE The consensus points described for these themes provide a roadmap for the implementation of digital health policy by all stakeholders, including governments. Implementation of these recommendations could have a significant impact by reducing fatalities and uniting countries on current and future battles against pandemics.Peer reviewe

TheAI Act meets general purpose AI: The good, the bad and the uncertain

The general approach of the Draft of AI Act (December 2022) expanded the scope to explicitly include General Purpose Artificial Intelligence. This paper presents an overview of the new proposals and analyzes their implications. Although the proposed regulation has the merit of regulating an expanding field that can be applied in different domains and on a large scale due to its dynamic context, it has some flaws. It is essential to ascertain whether we are dealing with a general-risk category or a specific category of high-risk. Moreover, we need to clarify the allocation of responsibilities and promote cooperation between different actors. Finally, exemptions to the regulation should be properly balanced to avoid liability gaps.FCT -Fundação para a Ciência e a Tecnologia(2021.07986

The Technology, Organization, and Environment Framework for Social Media Analytics in Government: The Cases of South Africa and Germany

This paper investigates factors influencing the adoption of social media analytics (SMA) for citizen relationship management (CzRM). Three real-world cases of government departments, two in South Africa and one in Germany, were investigated, and focus group discussions were conducted. The technological, organizational, and environmental (TOE) theory and qualitative content analysis guided the data analysis. The findings revealed that in all cases, staff usually conducted a manual analysis of social media and SMA had not been implemented sufficiently to realize its full potential. Insights were obtained from TOE and factors were identified that should be considered for improving the planning of SMA adoption in government. Data quality, methods and tools for SMA, and resources (e.g., skills and budget) were the most important factors identified for achieving success in SMA projects in government. The contribution is an improved understanding of the adoption of SMA for CzRM and can lead to effective monitoring of social media posts by citizens to improve service delivery and, hence, lead to more citizen-centric government

A privacy-preserving framework for smart context-aware healthcare applications

Smart connected devices are widely used in healthcare to achieve improved well-being, quality of life, and security of citizens. While improving quality of healthcare, such devices generate data containing sensitive patient information where unauthorized access constitutes breach of privacy leading to catastrophic outcomes for an individual as well as financial loss to the governing body via regulations such as the General Data Protection Regulation. Furthermore, while mobility afforded by smart devices enables ease of monitoring, portability, and pervasive processing, it introduces challenges with respect to scalability, reliability, and context awareness. This paper is focused on privacy preservation within smart context-aware healthcare emphasizing privacy assurance challenges within Electronic Transfer of Prescription. We present a case for a comprehensive, coherent, and dynamic privacy-preserving system for smart healthcare to protect sensitive user data. Based on a thorough analysis of existing privacy preservation models, we propose an enhancement to the widely used Salford model to achieve privacy preservation against masquerading and impersonation threats. The proposed model therefore improves privacy assurance for smart healthcare while addressing unique challenges with respect to context-aware mobility of such applications. © 2019 John Wiley & Sons, Ltd