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Register a new userCognitive Analysis of Security Threats on Social Networking Services: Slovakia in need of stronger action
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Added by
Jozef Michal Mintal
Publication date
2020
fields
Informatics Engineering
and research's language is
English
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This short paper examines some of the ongoing research at the UMB Data and Society Lab hosted at the Faculty of Political Science and International Relations at Matej Bel University. It begins with an introduction on the necessity of security threat identification on social networking services (SNSs), done by states. The paper follows with a general overview of selected projects of the Lab in this field, and afterwards it introduces a use case study focused on the announcement of the UK snap general election 2017. The main aim of this paper is to demonstrate some of the possibilities of social networking services analysis in the field of international relations, with an emphasis on disinformation and the necessity of identifying novel digital actors in Slovakia. We also outline an easy custom system tasked to collect social media data, and afterwards process it using various cognitive analytic methods.
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The Internet of Things (IoT) is already transforming industries, cities, and homes. The economic value of this transformation across all industries is estimated to be trillions of dollars and the societal impact on energy efficiency, health, and productivity are enormous. Alongside potential benefits of interconnected smart devices comes increased risk and potential for abuse when embedding sensing and intelligence into every device. One of the core problems with the increasing number of IoT devices is the increased complexity that is required to operate them safely and securely. This increased complexity creates new safety, security, privacy, and usability challenges far beyond the difficult challenges individuals face just securing a single device. We highlight some of the negative trends that smart devices and collections of devices cause and we argue that issues related to security, physical safety, privacy, and usability are tightly interconnected and solutions that address all four simultaneously are needed. Tight safety and security standards for individual devices based on existing technology are needed. Likewise research that determines the best way for individuals to confidently manage collections of devices must guide the future deployments of such systems.
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In this paper we take into account both social and linguistic aspects to perform demographic analysis by processing a large amount of tweets in Basque language. The study of demographic characteristics and social relationships are approached by applying machine learning and modern deep-learning Natural Language Processing (NLP) techniques, combining social sciences with automatic text processing. More specifically, our main objective is to combine demographic inference and social analysis in order to detect young Basque Twitter users and to identify the communities that arise from their relationships or shared content. This social and demographic analysis will be entirely based on the~automatically collected tweets using NLP to convert unstructured textual information into interpretable knowledge.
This article provides an overview of intimate threats: a class of privacy threats that can arise within our families, romantic partnerships, close friendships, and caregiving relationships. Many common assumptions about privacy are upended in the context of these relationships, and many otherwise effective protective measures fail when applied to intimate threats. Those closest to us know the answers to our secret questions, have access to our devices, and can exercise coercive power over us. We survey a range of intimate relationships and describe their common features. Based on these features, we explore implications for both technical privacy design and policy, and offer design recommendations for ameliorating intimate privacy risks.
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