The Socio-economic Impacts of Social Media Privacy and Security Challenges

© 2020, Springer Nature Singapore Pte Ltd. Privacy and Security are two major challenges faced by users on social media today. These challenges are experienced in diverse ways and forms by different types of users across the web. While technological solutions are usually implemented to address them, the effects have proven to be limited so far. Despite continuous deployment of technological solutions, the need to evaluate socio-economic impacts of these challenges have also become more imperative. Hence, this paper provides a critical review and analysis of socio-economic impacts of these social media challenges. The research findings reveal significant levels of negative socio-economic impacts and provides an evaluation framework towards defining the scope, thereby identifying appropriate measures for both addressing the challenges and curbing the socio-economic impacts. The findings also demonstrate the need for solutions beyond the use of technology, to employing and deploying solutions from social sciences which deals with behavioral issues and how to address them

Enhancing Public Healthcare Security: Integrating Cutting-Edge Technologies into Social Medical Systems

In a time when technology is present in every aspect of our lives, it is crucial to incorporate advanced solutions to protect sensitive medical data in Social Medical Systems (SMS). This study explores the need to improve security in public healthcare by using advanced technologies to strengthen the weaknesses in the growing field of Social Medical Systems. This study specifically examines the analysis of IoT-23 data using machine learning (ML) and deep learning (DL) methods, as technology and healthcare converge. The research highlights the increasing significance of technology in healthcare, specifically focusing on the revolutionary emergence of Social Medical Systems. As these interlinked networks reshape the provision of public healthcare services, security challenges such as data breaches, cyber threats, and privacy concerns become crucial barriers that require innovative solutions. The study utilizes a wide range of machine learning (ML) and deep learning (DL) techniques to examine IoT-23 data, offering a detailed comprehension of the security environment in Social Medical Systems. The chosen models comprise Support Vector Machines (SVM), Isolation Forest, Random Forest, Convolutional Neural Networks (CNN), and Autoencoder. The results and discussions focus on evaluating metrics such as accuracy, precision, recall, and F1 score. These metrics provide insights into how effective each model is in identifying vulnerabilities and potential threats in the IoT-23 dataset. The results contribute to the wider discussion on enhancing the security of public healthcare systems. They provide suggestions for incorporating anomaly detection, encryption protocols, and continuous monitoring to strengthen the security of Social Medical Systems. This research provides guidance for policymakers, healthcare practitioners, and technologists as they navigate the changing landscape of healthcare digitization. It advocates for the proactive integration of advanced technologies to ensure the security, privacy, and accessibility of healthcare information within the interconnected web of Social Medical Systems. DOI: https://doi.org/10.52710/seejph.48

Gerontechnology acceptance of smart homes: A systematic review and meta-analysis

Advances in preventive medicine and technology have beneficially affected longevity in the past decades. Unfortunately, longer life expectancy and declining fertility are likely to trigger an increasingly aging population, posing new challenges for social systems. Since aging populations affect the healthcare industry, providing convenient solutions and user-friendly elderly healthcare services is necessary to curb the growing demand by older adults. Several studies have proposed intelligent homes as potential solutions to support old age. However, such solutions raise the question of whether or not elderly persons intend to use smart homes and benefit from them. This paper examines the gerontechnology acceptance of intelligent homes by systematically reviewing previous studies on older people\u27s intention to use innovative home technology. The review was conducted from the Web of Science, Google Scholar, and Scopus, retrieving a thousand articles. Out of these, 40 are selected for the meta-analysis and systematic review. The integrative results showed an increasing intention of older adults to use smart home technology as they believe those innovative ways may improve independent living. However, attributes and drivers like privacy and perceived security show increasing heterogeneity and should draw more attention to prospective researchers

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

Online privacy: towards informational self-determination on the internet : report from Dagstuhl Perspectives Workshop 11061

The Dagstuhl Perspectives Workshop "Online Privacy: Towards Informational Self-Determination on the Internet" (11061) has been held in February 6-11, 2011 at Schloss Dagstuhl. 30 participants from academia, public sector, and industry have identified the current status-of-the-art of and challenges for online privacy as well as derived recommendations for improving online privacy. Whereas the Dagstuhl Manifesto of this workshop concludes the results of the working groups and panel discussions, this article presents the talks of this workshop by their abstracts

Literature Overview - Privacy in Online Social Networks

In recent years, Online Social Networks (OSNs) have become an important\ud part of daily life for many. Users build explicit networks to represent their\ud social relationships, either existing or new. Users also often upload and share a plethora of information related to their personal lives. The potential privacy risks of such behavior are often underestimated or ignored. For example, users often disclose personal information to a larger audience than intended. Users may even post information about others without their consent. A lack of experience and awareness in users, as well as proper tools and design of the OSNs, perpetuate the situation. This paper aims to provide insight into such privacy issues and looks at OSNs, their associated privacy risks, and existing research into solutions. The final goal is to help identify the research directions for the Kindred Spirits project

Visions and Challenges in Managing and Preserving Data to Measure Quality of Life

Health-related data analysis plays an important role in self-knowledge, disease prevention, diagnosis, and quality of life assessment. With the advent of data-driven solutions, a myriad of apps and Internet of Things (IoT) devices (wearables, home-medical sensors, etc) facilitates data collection and provide cloud storage with a central administration. More recently, blockchain and other distributed ledgers became available as alternative storage options based on decentralised organisation systems. We bring attention to the human data bleeding problem and argue that neither centralised nor decentralised system organisations are a magic bullet for data-driven innovation if individual, community and societal values are ignored. The motivation for this position paper is to elaborate on strategies to protect privacy as well as to encourage data sharing and support open data without requiring a complex access protocol for researchers. Our main contribution is to outline the design of a self-regulated Open Health Archive (OHA) system with focus on quality of life (QoL) data.Comment: DSS 2018: Data-Driven Self-Regulating System

Multiple multimodal mobile devices: Lessons learned from engineering lifelog solutions

For lifelogging, or the recording of one’s life history through digital means, to be successful, a range of separate multimodal mobile devices must be employed. These include smartphones such as the N95, the Microsoft SenseCam – a wearable passive photo capture device, or wearable biometric devices. Each collects a facet of the bigger picture, through, for example, personal digital photos, mobile messages and documents access history, but unfortunately, they operate independently and unaware of each other. This creates significant challenges for the practical application of these devices, the use and integration of their data and their operation by a user. In this chapter we discuss the software engineering challenges and their implications for individuals working on integration of data from multiple ubiquitous mobile devices drawing on our experiences working with such technology over the past several years for the development of integrated personal lifelogs. The chapter serves as an engineering guide to those considering working in the domain of lifelogging and more generally to those working with multiple multimodal devices and integration of their data