User-Upload Images on Content Sharing Sites

Users can establish photo privacy settings through the innovative Adaptive Privacy Policy Prediction (A3P) system.Social media platforms like Facebook and LinkedIn facilitate the sharing of vast amounts of personal information, leading many individuals to post their photos online in hopes of gaining attention for social interaction.Unfortunately, this trend has resulted in many public consumer photos being available, which raises serious concerns about privacy violations.The risk of identity theft, embarrassment, and even blackmail looms large as personal data proliferates.To address these challenges, it is essential to consider flexible privacy measures that can help protect users.This initiative aims to explore various privacy policy strategies to enhance the security of private data shared on social networking sites.While numerous researchers have focused on the broader privacy issues and social applications of online sharing, fewer studies specifically address the privacy challenges associated with photo sharing on these platforms.Users often share extensive information with many "friends," and as the volume of images shared increases, so does the potential for privacy infringements.Protecting this privacy is crucial for improving user satisfaction and ensuring a safer online experience.

Authors

Publication Details

Journal
Advances in Computational Sciences and Technology
Published
2024-12-26
DOI
https://doi.org/10.37622/acst/17.2.2024.77-101
Primary Topic
Image Retrieval and Classification Techniques
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

User-Upload Images on Content Sharing Sites

S. Ramesh Babu, M.D. Naresh, M M Devaiah
Advances in Computational Sciences and Technology
Image Retrieval and Classification Techniques
article

User-Upload Images on Content Sharing Sites

S. Ramesh Babu, M.D. Naresh, M M Devaiah
article en

Abstract

Users can establish photo privacy settings through the innovative Adaptive Privacy Policy Prediction (A3P) system.Social media platforms like Facebook and LinkedIn facilitate the sharing of vast amounts of personal information, leading many individuals to post their photos online in hopes of gaining attention for social interaction.Unfortunately, this trend has resulted in many public consumer photos being available, which raises serious concerns about privacy violations.The risk of identity theft, embarrassment, and even blackmail looms large as personal data proliferates.To address these challenges, it is essential to consider flexible privacy measures that can help protect users.This initiative aims to explore various privacy policy strategies to enhance the security of private data shared on social networking sites.While numerous researchers have focused on the broader privacy issues and social applications of online sharing, fewer studies specifically address the privacy challenges associated with photo sharing on these platforms.Users often share extensive information with many "friends," and as the volume of images shared increases, so does the potential for privacy infringements.Protecting this privacy is crucial for improving user satisfaction and ensuring a safer online experience.

Advances in Computational Sciences and TechnologyVol. 17(2)
Openalex Percentile: Top 75%
Image Retrieval and Classification Techniques
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.