Since the arrival of early social networking sites in the early 2000s, online social networking platforms have expanded exponentially, with the biggest names in social media in the mid-2010s being Facebook, Instagram, Twitter and Snapchat. The massive influx of personal information that has become available online and stored in the cloud has put user privacy at the forefront of discussion regarding the database's ability to safely store such personal information. The extent to which users and social media platform administrators can access user profiles has become a new topic of ethical consideration, and the legality, awareness, and boundaries of subsequent privacy violations are critical concerns in advance of the technological age.
A social network is a social structure made up of a set of social actors (such as individuals or organizations), sets of dyadic ties, and other social interactions between actors. Privacy concerns with social networking services is a subset of data privacy, involving the right of mandating personal privacy concerning storing, re-purposing, provision to third parties, and displaying of information pertaining to oneself via the Internet. Social network security and privacy issues result from the large amounts of information these sites process each day. Features that invite users to participate in—messages, invitations, photos, open platform applications and other applications are often the venues for others to gain access to a user's private information. In addition, the technologies needed to deal with user's information may intrude their privacy.
The advent of the Web 2.0 has caused social profiling and is a growing concern for internet privacy. Web 2.0 is the system that facilitates participatory information sharing and collaboration on the Internet, in social networking media websites like Facebook and MySpace. These social networking sites have seen a boom in their popularity beginning in the late 2000s. Through these websites many people are giving their personal information out on the internet.
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This advanced course will provide students with the knowledge to tackle the design of privacy-preserving ICT systems. Students will learn about existing technologies to prect privacy, and how to evalu
The course integrates concepts from media studies, machine learning, multimedia, and network science to characterize social practices and analyze content in platforms like Facebook, Twitter, and YouTu
Artificial intelligence, big data, and advances in computing power have triggered a technological revolution that may have enormous bearing on the workplace and the labor market. This course provides
Internet privacy involves the right or mandate of personal privacy concerning the storage, re-purposing, provision to third parties, and display of information pertaining to oneself via the Internet. Internet privacy is a subset of data privacy. Privacy concerns have been articulated from the beginnings of large-scale computer sharing and especially relate to mass surveillance enabled by the emergence of computer technologies. Privacy can entail either personally identifiable information (PII) or non-PII information such as a site visitor's behaviour on a website.
Introduces Attribute-Based Credentials and Mercurial Signatures, discussing privacy concerns with authentication and the importance of preserving user privacy.
Explores the challenges of protecting location privacy and various techniques to mitigate location-related inferences, highlighting the importance of trust assumptions and practical issues.
The advancement of face recognition technology has delivered substantial societal advantages. However, it has also raised global privacy concerns due to the ubiquitous collection and potential misuse of individuals' facial data. This presents a notable par ...
Graph Neural Networks (GNNs) have become a popular tool for learning on graphs, but their widespread use raises privacy concerns as graph data can contain personal or sensitive information. Differentially private GNN models have been recently proposed to p ...
Predictive models based on machine learning (ML) offer a compelling promise: bringing clarity and structure to complex natural and social environments. However, the use of ML poses substantial risks related to the privacy of their training data as well as ...