How to Prevent Social Media Platforms From Knowing the Images You Share With Friends

The surge in image sharing on social media platforms escalates private information extraction for commercial use, increasing user demand for privacy protection. However, the dynamics of group communication within online social networks and the image compression imposed by platforms present significant challenges to secure key exchange and reliable image sharing in existing solutions. In this paper, we propose PrivSocial to prevent social media platforms from extracting private information in images shared within group communications. Specifically, we propose two frameworks, a server-based framework and a subscription-based framework, making PrivSocial applicable to different social media platforms and providing users with optional security levels, enhancing the flexibility and efficiency. To achieve intra-group key agreement and ensure image privacy protection, both frameworks integrate optimized continuous group key agreement and a novel image encryption scheme resisting compression. We implement an Android-based Priv-raster application and deploy a prototype on Twitter. Furthermore, we evaluate the proposed encryption scheme, and experimental results show that it has efficient encryption and decryption performance while being resistant to jigsaw puzzle solver attacks. The multi-user simulation experiments also demonstrate that the processing time of a single user is mere milliseconds, and the scheme can efficiently support tens of thousands of groups.

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Publication Details

Journal
IEEE Transactions on Mobile Computing
Published
2025-02-05
DOI
https://doi.org/10.1109/tmc.2025.3538885
Citations
1
Primary Topic
Web Data Mining and Analysis
Type
article
Field-Weighted Citation Impact
3.25
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article

How to Prevent Social Media Platforms From Knowing the Images You Share With Friends

D. Liu, Song Bian, Zhenyu Guan, Dawei Li et al.
1 citations
IEEE Transactions on Mobile Computing
Web Data Mining and Analysis
3.25
article

How to Prevent Social Media Platforms From Knowing the Images You Share With Friends

D. Liu, Song Bian, Zhenyu Guan, Dawei Li, Quan Liu, Yiming Guo
article en
1 citations

Abstract

The surge in image sharing on social media platforms escalates private information extraction for commercial use, increasing user demand for privacy protection. However, the dynamics of group communication within online social networks and the image compression imposed by platforms present significant challenges to secure key exchange and reliable image sharing in existing solutions. In this paper, we propose PrivSocial to prevent social media platforms from extracting private information in images shared within group communications. Specifically, we propose two frameworks, a server-based framework and a subscription-based framework, making PrivSocial applicable to different social media platforms and providing users with optional security levels, enhancing the flexibility and efficiency. To achieve intra-group key agreement and ensure image privacy protection, both frameworks integrate optimized continuous group key agreement and a novel image encryption scheme resisting compression. We implement an Android-based Priv-raster application and deploy a prototype on Twitter. Furthermore, we evaluate the proposed encryption scheme, and experimental results show that it has efficient encryption and decryption performance while being resistant to jigsaw puzzle solver attacks. The multi-user simulation experiments also demonstrate that the processing time of a single user is mere milliseconds, and the scheme can efficiently support tens of thousands of groups.

IEEE Transactions on Mobile ComputingVol. 24(7)
Beihang University (CN)
Reduced inequalities
Openalex Percentile: Top 11%
Web Data Mining and Analysis
3.25
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