What is it about?
In the last past years, Online Social Networks have shifted the traditional paradigm of human interactions and communication into a digital representation of their relationships and daily activities. Ranging from blogging services to social communities, multimedia sharing, and virtual worlds, the demand on such online services grows continually. They supply services to a broad range of users of all ages, various social backgrounds, and users with limited technical skills. This article presents a quantitative comparison of 24 online social networks based on different criteria (architecture, storage, security, encryption, ...) to understand the functioning of social networks, the privacy issues they have, and to understand the different techniques used to protect the privacy.
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Why is it important?
This article not only compares between 24 systems but it reviews different topics related to privacy and social networks: privacy violations and their mitigation techniques and approaches, existing information privacy laws and regulations, privacy principles, mitigation approaches to protect privacy,
Read the Original
This page is a summary of: Privacy Analysis on Microblogging Online Social Networks, ACM Computing Surveys, July 2019, ACM (Association for Computing Machinery), DOI: 10.1145/3321481.
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IPAM: Information Privacy Assessment Metric in Microblogging Online Social Networks
A large amount of sensitive data is transferred, stored, processed, and analyzed daily in Online Social Networks (OSNs). Thus, an effective and efficient evaluation of the privacy level provided in such services is necessary to meet user expectations and comply with the requirement of the applicable laws and regulations. Several prior works have proposed mechanisms for evaluating and calculating privacy scores in OSNs. However, current models are system-specific and assess privacy only from the user’s perspective. There is still a lack of a universal model that can quantify the level of privacy and compare between different systems. In this paper, we propose a generic framework to (i) guide the development of privacy metrics and (ii) to measure and assess the privacy level of OSNs, more specifically microblogging systems. The present study develops an algorithmic model to compute privacy scores based on the impact of privacy and security requirements, accessibility, and difficulty of information extraction. The proposed framework aims to provide users as well as system providers with a measure of how much the investigated system is protecting privacy. It allows also comparing the privacy protection level between different systems. The privacy score framework has been tested using real microblogging social networks and the results show the potential of the proposed privacy scoring framework.
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