A Survey on Privacy in Social Media

Abstract

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. These data provide 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 research area and has attracted increasing attention recently. 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 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) social graphs and privacy, (2) authors in social media and privacy, (3) profile attributes and privacy, (4) location and privacy, and (5) recommendation systems and privacy. We also discuss open problems and future research directions regarding user privacy issues in social media.

Automatic de-identification of data download packages

“…User privacy in social media is an emerging research area and has attracted increasing attention recently. To avoid privacy attacks, like identity disclosure and attribute disclosure, publishers of social media data are obliged to protect users' privacy by anonymizing these data before they are published publicly [3]. Anonymizing social media data is a challenging task due to their heterogeneous, highly unstructured and noisy nature [3].…”

“…To avoid privacy attacks, like identity disclosure and attribute disclosure, publishers of social media data are obliged to protect users' privacy by anonymizing these data before they are published publicly [3]. Anonymizing social media data is a challenging task due to their heterogeneous, highly unstructured and noisy nature [3]. Commonly used statistical disclosure control approaches [10,25,27,30,31] are designed for relational and tabular data and cannot be directly applied to social media data.…”

“…These data rather contain person names, usernames or IDs, email addresses and locations [8,9], but in fact there is limited work on the types of personal identifiable information (PII) that may be present in textual social media data and how these should be removed [4,9]. Yet, removing such information has been shown to be far from sufficient in preserving privacy since users' identity or attributes may be inferred from the public data available on social media platforms [2,3,19,23]. Finally, social media data may also consist of visual content.…”

Exploiting All Samples in Low-Resource Sentence Classification: Early Stopping and Initialization Parameters

“…First, target data are difficult to collect. For example, data collection can be difficult when it contains personal information [4], [5], such as social-media profile, or has little presence on the Web, such as African languages [6], [7]. Second, data labeling is expensive.…”

Privacy-Preserving Social Media With Unlinkability and Disclosure

“…The explosive growth of media over the past decade has 2 drastically transformed the World Wide Web, enabling 3 billions of people to freely engage in various activities 4 within a richly heterogeneous environment [3]. In par-5 ticular, the emergence of social media (SM) networks, 6 such as Twitter and Facebook, has accelerated the speed 7 of information transmission [23].…”