Privacy preserving social network data publication

The introduction of online social networks (OSN) has transformed the way people connect and interact with each other as well as share information. OSN have led to a tremendous explosion of network-centric data that could be harvested for better understanding of interesting phenomena such as sociological and behavioural aspects of individuals or groups. As a result, online social network service operators are compelled to publish the social network data for use by third party consumers such as researchers and advertisers. As social network data publication is vulnerable to a wide variety of reidentification and disclosure attacks, developing privacy preserving mechanisms are an active research area. This paper presents a comprehensive survey of the recent developments in social networks data publishing privacy risks, attacks, and privacy-preserving techniques. We survey and present various types of privacy attacks and information exploited by adversaries to perpetrate privacy attacks on anonymized social network data. We present an in-depth survey of the state-of-the-art privacy preserving techniques for social network data publishing, metrics for quantifying the anonymity level provided, and information loss as well as challenges and new research directions. The survey helps readers understand the threats, various privacy preserving mechanisms, and their vulnerabilities to privacy breach attacks in social network data publishing as well as observe common themes and future directions

Bibliometric Survey of Privacy of Social Media Network Data Publishing

We are witness to see exponential growth of the social media network since the year 2002. Leading social media networking sites used by people are Twitter, Snapchats, Facebook, Google, and Instagram, etc. The latest global digital report (Chaffey and Ellis-Chadwick 2019) states that there exist more than 800 million current online social media users, and the number is still exploding day by day. Users share their day to day activities such as their photos and locations etc. on social media platforms. This information gets consumed by third party users, like marketing companies, researchers, and government firms. Depending upon the purpose, there is a possibility of misuse of the user\u27s personal & sensitive information. Users\u27 sensitive information breaches can further utilized for building a personal profile of individual users and also lead to the unlawful tracing of the individual user, which is a major privacy threat. Thus it is essential to first anonymize users\u27 information before sharing it with any of the third parties. Anonymization helps to prevent exposing sensitive information to the third party and avoids its misuse too. But anonymization leads to information loss, which indirectly affects the utility of data; hence, it is necessary to balance between data privacy and utility of data. This research paper presents a bibliometric analysis of social media privacy and provides the exact scope for future research. The research objective is to analyze different research parameters and get insights into privacy in Social Media Network (OSN). The research paper provides visualization of the big picture of research carried on the privacy of the social media network from the year 2010 to 2019 (covers the span of 19 years). Research data is taken from different online sources such as Google Scholar, Scopus, and Research-gate. Result analysis has been carried out using open source tools such as Gephi and GPS Visualizer. Maximum publications of privacy of the social media network are from articles and conferences affiliated to the Chinese Academy of Science, followed by the Massachusetts Institute of Technology. Social networking is a frequently used keyword by the researchers in the privacy of the online social media network. Major Contribution in this subject area is by the computer science research community, and the least research contribution is from art and science. This study will clearly give an understanding of contributions in the privacy of social media network by different organizations, types of contributions, more cited papers, Authors contributing more in this area, the number of patents in the area, and overall work done in the area of privacy of social media network

Social Network Privacy Models

Privacy is a vital research field for social network (SN) sites (SNS), such as Facebook, Twitter, and Google+, where both the number of users and the number of SN applications are sharply growing. Recently, there has been an exponential increase in user-generated text content, mainly in terms of posts, tweets, reviews, and messages on SN. This increase in textual information introduces many problems related to privacy. Privacy is susceptible to personal behavior due to the shared online data structure of SNS. Therefore, this study will conduct a systematic literature review to identify and discuss the main privacy issues associated with SN, existing privacy models and the limitations and gaps in current research into SN privacy

Recent advances in mobile touch screen security authentication methods: a systematic literature review

The security of the smartphone touch screen has attracted considerable attention from academics as well as industry and security experts. The maximum security of the mobile phone touch screen is necessary to protect the user’s stored information in the event of loss. Previous reviews in this research domain have focused primarily on biometrics and graphical passwords while leaving out PIN, gesture/pattern and others. In this paper, we present a comprehensive literature review of the recent advances made in mobile touch screen authentication techniques covering PIN, pattern/gesture, biometrics, graphical password and others. A new comprehensive taxonomy of the various multiple class authentication techniques is presented in order to expand the existing taxonomies on single class authentication techniques. The review reveals that the most recent studies that propose new techniques for providing maximum security to smartphone touch screen reveal multi-objective optimization problems. In addition, open research problems and promising future research directions are presented in the paper. Expert researchers can benefit from the review by gaining new insights into touch screen cyber security, and novice researchers may use this paper as a starting point of their inquir

Seeding with Differentially Private Network Information

When designing interventions in public health, development, and education, decision makers rely on social network data to target a small number of people, capitalizing on peer effects and social contagion to bring about the most welfare benefits to the population. Developing new methods that are privacy-preserving for network data collection and targeted interventions is critical for designing sustainable public health and development interventions on social networks. In a similar vein, social media platforms rely on network data and information from past diffusions to organize their ad campaign and improve the efficacy of targeted advertising. Ensuring that these network operations do not violate users' privacy is critical to the sustainability of social media platforms and their ad economies. We study privacy guarantees for influence maximization algorithms when the social network is unknown, and the inputs are samples of prior influence cascades that are collected at random. Building on recent results that address seeding with costly network information, our privacy-preserving algorithms introduce randomization in the collected data or the algorithm output, and can bound each node's (or group of nodes') privacy loss in deciding whether or not their data should be included in the algorithm input. We provide theoretical guarantees of the seeding performance with a limited sample size subject to differential privacy budgets in both central and local privacy regimes. Simulations on synthetic and empirical network datasets reveal the diminishing value of network information with decreasing privacy budget in both regimes.Comment: Preliminary version in AAMAS 2023: https://dl.acm.org/doi/10.5555/3545946.3599081 -- Code and data: https://github.com/aminrahimian/dp-inf-ma