How Do Default Privacy Settings on Social Media Apps Match People’s Actual Preferences?

Alan, Alper T.; Al-Arnaout, Zakwan; Topcu, Ahmet E.; Zaki, Chamseddine; Shdefat, Ahmed Younes; Elbaşi, Ersin

doi:10.1109/icecta57148.2022.9990282

Cited by 3 publications

(3 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Social networking sites provide a variety of privacy features and options to allow users to manage personal information and enhance security. These privacy settings allow users to adjust the visibility of their personal information and decide who can see their profiles, posts, and other shared content (Alan et al, 2022). By adjusting these settings, users can adjust their privacy settings to their comfort level.…”

Section: Literature Reviewmentioning

confidence: 99%

User Awareness of Security and Privacy in Social Networking Sites

Krishnappa

2023

IJEAST

View full text Add to dashboard Cite

The rapid expansion and widespread adoption of social networking sites have created new security and privacy challenges for individuals. This study will assess users' awareness of the security and privacy measures and their understanding of potential risks and vulnerabilities. The study uses a quantitative survey to collect data on users' perceptions, understandings, and experiences about security and privacy on social sites. The results will be analyzed to identify trends, knowledge gaps, and user perceptions, providing insight into the current landscape of user perceptions and insights. The outcome of this study will be useful in formulating plans and recommendations to improve security measures and promote responsible online behavior among social network administrators, policymakers, and users. This study aims to provide users with the necessary knowledge to adequately protect themselves in the digital age by highlighting their awareness and understanding of security and privacy on social networks.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

User Awareness of Security and Privacy in Social Networking Sites

Krishnappa

2023

IJEAST

View full text Add to dashboard Cite

show abstract

“…Over time, there has been a growing trend of individuals sharing sensitive personal information on online social networks [12]. While these networks do provide users with some control over their privacy settings, such as managing who they share information with and enabling or disabling location services, access control policies have proven to be notoriously difficult to configure correctly [13]. This raises the question of whether online social network users' privacy matches their actual sharing intentions.…”

Section: Online Social Network Privacy Settingsmentioning

confidence: 99%

A Study on Exploring the Level of Awareness of Privacy Concerns and Risks

Nguyen,

Yeates,

et al. 2023

Applied Sciences

View full text Add to dashboard Cite

With the recent increase in phishing attacks and other kinds of malicious activities, increasing the awareness of security and privacy awareness is arguably one of the best proven ways of preventing these kinds of threats. The main challenge in security and privacy awareness is the end user’s awareness of aspects of privacy and security they give up when using the Internet. Thus, this study focuses on identifying and discussing the growing threats of Internet usage and the lack of privacy and security knowledge of the everyday person. This study presents the results of a survey conducted to determine discrepancies between what rights users think they sign away when they agree to terms of service versus what rights they actually give away. It is hypothesized that people are genuinely unaware of what rights they are giving up, especially since they are signing the terms of service without reading the fine print. In this study, the terms of service were presented to respondents, and they answered questions on what they thought they were giving up, but once they answered the questions, they were explicitly told whether they truly knew what rights they signed away. The experimental results of this study examine how much knowledge the everyday person lacks with respect to the privacy policies they sign. All of this is ultimately used to examine possible flaws in the system. The experimental results illustrate the results of the survey. Based on the total surveys completed, the average score was 62%. This means that out of 10 clauses described in a given terms of service document or privacy policy, people are truly unaware of at least 4 of these statements.

show abstract

“…The reasons behind it are multiple. For example, these control mechanisms may not be directly accessible by the users (Chen et al 2019), time-consuming to use (Lipford, Besmer, and Watson 2008), or complex to apply so that they match users' privacy preferences (Alan et al 2022). Not exercising such control can, however, have severe consequences.…”

Section: Introductionmentioning

confidence: 99%

SensitivAlert: Image Sensitivity Prediction in Online Social Networks Using Transformer-Based Deep Learning Models

Kqiku,

Reinhardt

2024

ICWSM

View full text Add to dashboard Cite

Billions images are shared daily on social networks. When shared with an inappropriate audience, user-generated images can, however, compromise users' privacy and may have severe consequences, such as dismissals. To address this issue, different solutions were proposed, ranging from graphical user interfaces to Deep Learning (DL) models to alert users based on image sensitivity prediction. Although these models show promising results, they are evaluated on datasets relying on small participants' samples. To address this limitation, we first introduce SensitivAlert, a dataset that re-annotates the previously annotated images from two existing datasets, but using a German-speaking cohort of 907 participants. We then leverage it to classify images according to two sensitivity classes---private or public---using recent transformer-based DL models. In our evaluation, we first consider consensus-based generic models using our dataset as benchmark based on image content itself and its associated user tags. Moreover, we show that our fine-tuned models trained on our dataset better reflect users' image privacy conceptions. We finally focus on individual user's privacy estimation by investigating three approaches: (1) a generic approach based on participants' consensus for fine-tuning, (2) a user-wise approach based on user's privacy preferences only, and (3) a hybrid approach that combines individual preferences with consensus-based preferences. Our results finally show that the generic and hybrid approaches outperform the user-wise one for most users, thus ensuring the feasibility of image privacy prediction preferences at the individuals' level.

show abstract

How Do Default Privacy Settings on Social Media Apps Match People’s Actual Preferences?

Cited by 3 publications

References 12 publications

User Awareness of Security and Privacy in Social Networking Sites

User Awareness of Security and Privacy in Social Networking Sites

A Study on Exploring the Level of Awareness of Privacy Concerns and Risks

SensitivAlert: Image Sensitivity Prediction in Online Social Networks Using Transformer-Based Deep Learning Models

Contact Info

Product

Resources

About