# Privacy in Social Media: Identification, Mitigation and Applications

arXiv: https://arxiv.org/abs/1808.02191 · HTML: https://mlsanity.com/paper/1808.02191/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Aug 2018.

## Built on by (most cited first, 19 of 19)

- [Trustworthy AI: From Principles to Practices](https://mlsanity.com/paper/2110.01167/) (2021)
- [Membership Inference Attack on Graph Neural Networks](https://mlsanity.com/paper/2101.06570/) (2021)
- [Socially Responsible AI Algorithms: Issues, Purposes, and Challenges](https://mlsanity.com/paper/2101.02032/) (2021)
- [A Survey on Privacy in Graph Neural Networks: Attacks, Preservation, and Applications](https://mlsanity.com/paper/2308.16375/) (2023)
- [A Survey of Code-switched Arabic NLP: Progress, Challenges, and Future Directions](https://mlsanity.com/paper/2501.13419/) (2025)
- [Simultaneous Unlearning of Multiple Protected User Attributes From Variational Autoencoder Recommenders Using Adversarial Training](https://mlsanity.com/paper/2410.20965/) (2024)
- [What Your Posts Reveal: A Benchmark and Agentic Framework for User-Level Privacy Leakage on Social Media](https://mlsanity.com/paper/2606.06784/) (2026)
- [Data Leakage via Access Patterns of Sparse Features in Deep Learning-based Recommendation Systems](https://mlsanity.com/paper/2212.06264/) (2022)
- [Deep Privacy Funnel Model: From a Discriminative to a Generative Approach with an Application to Face Recognition](https://mlsanity.com/paper/2404.02696/) (2024)
- [SoK: Can Synthetic Images Replace Real Data? A Survey of Utility and Privacy of Synthetic Image Generation](https://mlsanity.com/paper/2506.19360/) (2025)
- [Learning to Attack: Uncovering Privacy Risks in Sequential Data Releases](https://mlsanity.com/paper/2510.24807/) (2025)
- [On Privacy and Confidentiality of Communications in Organizational Graphs](https://mlsanity.com/paper/2105.13418/) (2021)
- [Exploiting All Samples in Low-Resource Sentence Classification: Early Stopping and Initialization Parameters](https://mlsanity.com/paper/2111.06971/) (2021)
- [Caring Without Sharing: A Federated Learning Crowdsensing Framework for Diversifying Representation of Cities](https://mlsanity.com/paper/2201.07980/) (2022)
- [Discussion about Attacks and Defenses for Fair and Robust Recommendation System Design](https://mlsanity.com/paper/2210.07817/) (2022)
- [ST-DPGAN: A Privacy-preserving Framework for Spatiotemporal Data Generation](https://mlsanity.com/paper/2406.03404/) (2024)
- [Bayes-Nash Generative Privacy Against Membership Inference Attacks](https://mlsanity.com/paper/2410.07414/) (2024)
- [Evidencing Unauthorized Training Data from AI Generated Content using Information Isotopes](https://mlsanity.com/paper/2503.20800/) (2025)
- [NLP Privacy Risk Identification in Social Media (NLP-PRISM): A Survey](https://mlsanity.com/paper/2602.15866/) (2026)

## Abstract (arXiv)

The increasing popularity of social media has attracted a huge number of people to participate in numerous activities on a daily basis. This results in tremendous amounts of rich user-generated data. This data provides opportunities for researchers and service providers to study and better understand users' behaviors and further improve the quality of the personalized services. Publishing user-generated data risks exposing individuals' privacy. Users privacy in social media is an emerging task and has attracted increasing attention in recent years. These works study privacy issues in social media from the two different points of views: identification of vulnerabilities, and mitigation of privacy risks. Recent research has shown the vulnerability of user-generated data against the two general types of attacks, identity disclosure and attribute disclosure. These privacy issues mandate social media data publishers to protect users' privacy by sanitizing user-generated data before publishing it. Consequently, various protection techniques have been proposed to anonymize user-generated social media data. There is a vast literature on privacy of users in social media from many perspectives. In this survey, we review the key achievements of user privacy in social media. In particular, we review and compare the state-of-the-art algorithms in terms of the privacy leakage attacks and anonymization algorithms. We overview the privacy risks from different aspects of social media and categorize the relevant works into five groups 1) graph data anonymization and de-anonymization, 2) author identification, 3) profile attribute disclosure, 4) user location and privacy, and 5) recommender systems and privacy issues. We also discuss open problems and future research directions for user privacy issues in social media.