Understanding the Factors that Influence Adoption of Privacy Protection Features in Online Social Networks

2021 ◽  
pp. 1-19
Author(s):  
Deepesh Kumar Srivastava ◽  
Basav Roychoudhury
Keyword(s):  
Social Networks ◽  
Online Social Networks ◽  
Privacy Protection

Privacy in Online Social Networks

2021 ◽  
pp. 2078-2109
Author(s):  
Ramanpreet Kaur ◽  
Tomaž Klobučar ◽  
Dušan Gabrijelčič
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Online Social Networks ◽  
Privacy Protection ◽  
Privacy Preservation ◽  
Mitigation Strategies ◽  
Research Issues ◽  
Privacy Requirements ◽  
Privacy Threats

This chapter is concerned with the identification of the privacy threats to provide a feedback to the users so that they can make an informed decision based on their desired level of privacy. To achieve this goal, Solove's taxonomy of privacy violations is refined to incorporate the modern challenges to the privacy posed by the evolution of social networks. This work emphasizes on the fact that the privacy protection should be a joint effort of social network owners and users, and provides a classification of mitigation strategies according to the party responsible for taking these countermeasures. In addition, it highlights the key research issues to guide the research in the field of privacy preservation. This chapter can serve as a first step to comprehend the privacy requirements of online users and educate the users about their choices and actions in social media.

CP2: Cryptographic privacy protection framework for online social networks

2013 ◽  
Vol 39 (7) ◽  
pp. 2282-2298 ◽  
Author(s):  
Fatemeh Raji ◽  
Ali Miri ◽  
Mohammad Davarpanah Jazi
Keyword(s):  
Social Networks ◽  
Online Social Networks ◽  
Privacy Protection

Effectiveness and Users’ Experience of Face Blurring as a Privacy Protection for Sharing Photos via Online Social Networks

2017 ◽  
Vol 61 (1) ◽  
pp. 803-807 ◽  
Author(s):  
Yifang Li ◽  
Nishant Vishwamitra ◽  
Hongxin Hu ◽  
Bart P. Knijnenburg ◽  
Kelly Caine
Keyword(s):  
Social Networks ◽  
Social Presence ◽  
Online Social Networks ◽  
Privacy Protection ◽  
Online Experiment ◽  
Photo Sharing ◽  
Privacy Issues

Photo sharing on online social networks (OSNs) can cause privacy issues. Face blurring is one strategy to increase privacy while still allowing users to share photos. To explore the potential blurring has as a privacy-enhancing technology for OSN photos, we conducted an online experiment with 47 participants to evaluate the effectiveness of face blurring compared to the original photo (as-is), and users’ experience (satisfaction, information sufficiency, enjoyment, social presence, and filter likeability). Users’ experience ratings for face blurring were positive, indicating blurring may be an acceptable way to modify photos from the users’ perspective. However, from a privacy-enhancement perspective, while face blurring may be useful in some situations, such as those where the person in the photo is unknown to the viewer, in other cases, such as in an OSN where the person in the image is known to the viewer, face blurring does not provide privacy protection.

scholarly journals Learning Effective Feature Representation against User Privacy Protection on Social Networks

2020 ◽  
Vol 10 (14) ◽  
pp. 4835
Author(s):  
Cheng-Te Li ◽  
Zi-Yun Zeng
Keyword(s):  
Social Networks ◽  
Online Social Networks ◽  
Privacy Protection ◽  
Data Privacy ◽  
Personal Information ◽  
Representation Learning ◽  
Feature Representation ◽  
User Privacy ◽  
Node Embeddings

Users pay increasing attention to their data privacy in online social networks, resulting in hiding personal information, such as profile attributes and social connections. While network representation learning (NRL) is widely effective in social network analysis (SNA) tasks, it is essential to learn effective node embeddings from privacy-protected sparse and incomplete network data. In this work, we present a novel NRL model to generate node embeddings that can afford data incompleteness coming from user privacy protection. We propose a structure-attribute enhanced matrix (SAEM) to alleviate data sparsity and develop a community-cluster informed NRL method, c2n2v, to further improve the quality of embedding learning. Experiments conducted across three datasets, three simulations of user privacy protection, and three downstream SNA tasks exhibit the promising performance of our NRL model SAEM+c2n2v.

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