Social Network Service Providers Research Articles

Privacy by design. User-held data model as a basis for Privacy and Personal Data protection in social networks

The article reveals the concept and features of privacy by design approach. An analysis of the legislation of the European Union, which regulates the obligation to comply with mentioned concept, namely the provisions of the General Data Protection Regulation, was carried out. Within the framework of the concept of privacy by design, the concepts and pecularities of using user-held data model are studied, which aims to change the legal regulation and technical support of information security as well as the security of personal data in social networks.
 In order to achieve the goal of the research, the need to introduce the specified model of data protection was analyzed – namely practical problems of personal data protection within the peculiarities of the market of social network service providers. The need of the interpretation of personal data as an object of property right was outlined, as well as the interpretation of the purpose of the meta data itself as personal information about a person that can be used to recognize or track a person separately or in combination with other information, which is directly stated in the OMB Act No. A-130, however, is neither interpreted similarly in the Data Protection Regulation nor in the legislation of Ukraine. The main advantages of implementing a user-held data model are presented, namely the advantages for data owners, businesses, the state, and cloud service providers for providing and maintaining the model.
 Legislation in the field of personal data protection in the EU, in particular in the field of social networks, is more complex at the supranational and national level. It is provided with a separate regulatory and organizational mechanisms, which are different from the one that exists today in Ukraine. The mechanism for the protection of personal data in the European Union provides the creation of independent public authority that is responsible for the implementation and compliance with the requirements of the General Data Protection Regulation. The article highlights the need to create a single separate body in the field of personal data protection in Ukraine, which will be able to perform the function of monitoring and ensuring compliance with the requirements of the relevant legislation, in particular, compliance with privacy by design.

Exploring the Privacy Paradox in Social Network Users: A Double-Entry Mental Accounting Theory Perspective

Social networking service (SNS) users often express great concern for their personal privacy, yet continue to disclose personal information on these platforms. This privacy paradox between privacy concerns and disclosure behavior has drawn widespread academic attention. In this study, we use the double-entry mental accounting theory to construct a theoretical model and conduct an in-depth analysis of the privacy paradox phenomenon and its causes through empirical verification. Our research shows a significant positive correlation between perceived benefits and users’ intention to disclose privacy, while perceived risks and users’ intention to disclose privacy are significantly negatively correlated. The double-entry mental accounting theory plays a crucial role in mediating the relationship between perceived values and users’ intention to disclose privacy. Furthermore, we found that information sensitivity negatively regulates the relationship between perceived risks, the pleasure attenuation coefficient α, the pain buffering coefficient β, and the intention to disclose privacy. Our study provides theoretical and empirical information on the reasons for the privacy paradox and offers insights for social networking service providers to optimize their services.

Privacy paradox for location tracking in mobile social networking apps: The perspectives of behavioral reasoning and regulatory focus

Social networking services (SNSs) have been collecting and analyzing users' personal data for the sake of personalization and customization, raising massive privacy concerns. Among the various types of privacy, personal location data are considered most sensitive, but are critical for the location-based features in mobile SNS applications. Drawing on behavioral reasoning theory and accounting for the privacy paradox, this study investigates the heterogeneous psychological mechanisms supporting and discouraging mobile SNS users from enabling location tracking in SNS applications. Based on a survey of young internet users in China, our analysis shows that, first, reasons for location tracking are insignificantly associated with intention to allow location tracking but reasons against are negatively related to intention. Furthermore, for individuals with higher desire to share, reasons for and attitude have the stronger associations with intention, while the moderating effect of desire for privacy is insignificant. We make research contributions by examining the antecedents of the reasoning process, as well as the moderating role of desire to share, to account for the privacy paradox. We also contribute to behavioral reasoning theory by introducing regulatory focus-based antecedents to the reasoning process. We offer key practical suggestions for SNS providers and policymakers.

Threat Modelling and Detection Using Semantic Network for Improving Social Media Safety

Social media provides a free space to users to post their information, opinions, feelings, etc. Also, it allows users to easily and simultaneously communicate with each other. As a result, threat detection in social media is critical for ensuring the user’s safety and preventing suspicious activities such as criminal behavior, hate speech, ethnic conflicts and terrorist plots. These suspicious activities have a negative impact on the community’s life and cause tension and social unrest among individuals in both inside and outside of cyberspace. Furthermore, with the recent popularity of social networking sites, the number of discussions containing threats is increasing, causing fear in various parties, whether at the individual or state level. Moreover, these social networking service providers do not have complete control over the content that users post. In this paper, we propose to design a threat detection model on Twitter using a semantic network. To achieve this aim, we designed a threat semantic network, named, ThrNet that will be integrated in our proposed threat detection model called, DetThr. We compared the performance of our model (DetThr) with a set of well-known Machine Learning algorithms. Results show that the DetThr model achieves an accuracy of 76% better than Machine Learning algorithms. It works well with an error rate of forecasting threatening tweet messages as non-threatening (false negatives) is about 29%, while the error rate of forecasting non-threatening tweet messages as threatening (false positives) is about 19%.

Avoiding the Diffusion of Responsibility in Social Networking Groups: A Field Experiment on Responses to Online Help-Request Referrals

Help-request referral (HRR) campaigns are being increasingly adopted by online service providers to attract online traffic and engage new users. In these campaigns, participants are required to collect a specific amount of help from their social network in exchange for attractive rewards. Participants often face challengesin obtaining positive feedback from their social networking groups due to the diffusion of responsibility. Based on the triangle model of responsibility, this study proposes and empirically tests a research model that examines two key determinants (i.e., request personalization and relational closeness) of recipients’ perceived responsibility that further shape their responses to HRR. Our field experiment empirically supports the research hypotheses. Overall, this study enriches the literature on online social referrals by focusing on HRR, where social referrals are broadcasted and senders reap referral benefits. The study also provides practical insights for HRR designers and social networking service providers.

Systematic Machine Translation of Social Network Data Privacy Policies

With the growing popularity of online social networks, one common desire of people is to use of social networking services for establishing social relations with others. The boom of social networking has transformed common users into content (data) contributors. People highly rely on social sites to share their ideas and interests and express opinions. Social network sites store all such activities in a data form and exploit the data for various purposes, e.g., marketing, advertisements, product delivery, product research, and even sentiment analysis, etc. Privacy policies primarily defined in Natural Language (NL) specify storage, usage, and sharing of the user’s data and describe authorization, obligation, or denial of specific actions under specific contextual conditions. Although these policies expressed in Natural Language (NL) allow users to read and understand the allowed (or obliged or denied) operations on their data, the described policies cannot undergo automatic control of the actual use of the data by the entities that operate on them. This paper proposes an approach to systematically translate privacy statements related to data from NL into a controlled natural one, i.e., CNL4DSA to improve the machine processing. The methodology discussed in this work is based on a combination of standard Natural Language Processing (NLP) techniques, logic programming, and ontologies. The proposed technique is demonstrated with a prototype implementation and tested with policy examples. The system is tested with a number of data privacy policies from five different social network service providers. Predominantly, this work primarily takes into account two key aspects: (i) The translation of social networks’ data privacy policy and (ii) the effectiveness and efficiency of the developed system. It is concluded that the proposed system can successfully and efficiently translate any common data policy based on an empirical analysis performed of the obtained results.

How Do Passive Social Networking Services Use and Its Antecedents Affect Users' Continuance Intention? An Empirical Study of WeChat Use.

Although social networking services (SNSs) have attracted billions of people to maintain and extend their social relationships online, more and more passive usage behaviors have been found during the daily SNS usage. The aim of this paper is to investigate how SNS users' continuance intention is affected by passive SNS use, subjective well-being, as well as perceived concern regarding privacy and impression management. A research model was developed according to the proposed hypotheses, and then partial least square (PLS) SEM was adopted to empirically assess the valid data collected from 389 WeChat users. The findings show that passive SNS use cannot significantly undermine continuance intention, but it has a negative moderating effect on the relationship between subjective well-being and continuance intention. Besides, the results of empirical research also reveal the antecedents of passive SNS use and subjective well-being from the perspectives of privacy concern and impression management concern, and thereby present the mechanism underlying users' continuance intention. This study enriches the SNS literature by indicating the moderating role of passive SNS use in the process of SNS usage, which facilitates the understanding regarding how users' continuance intention can be influenced when they use SNSs passively. This study can help SNS providers to better understand the factors affecting users' continuance intention in the case of passive SNS use, and then formulating effective strategies for retaining users and avoiding passive usage behaviors.

Research on Digital Information Privacy Behavior of Social Network Users Based on Evolutionary Game

This article takes social network services as the research object and mainly conducts two aspects of research: first, based on previous research, combining the behavior characteristics of users in the social network environment related to digital information privacy, the digital information privacy of users pays attention to the influential factors of users’ digital information privacy information disclosure behavior, summarize and refine them, establish theoretical research models, and then use structural equation modelling to empirically analyze the significance of each influence path; then, based on the first part of the empirical research, the evolutionary game theory was used to analyze the interests of digital information privacy between social network users and service providers. This paper also studies the evolution of the willingness of each parameter to provide digital information to users under variable expected return conditions and uses MATLAB to analyze its evolutionary trends. It is found that the regulatory intensity coefficient, information leakage loss, and information sensitivity are both for users and websites. It has an important impact, and the loss of information leakage and information sensitivity can affect the evolution direction of users’ willingness to provide digital information and change the speed of website evolution, and the regulatory intensity coefficient is the opposite.

Social Networks as New Public Forums?

Abstract The article provides a comparative overview on recent case law developments concerning the so-called deplatforming, calling for a more in-depth consideration of the role of social network service providers. By comparing the US and Italian scenarios, it delves into the question on whether these operators should be equalized to public forums or State actors and then subject to stricter regulation with respect to content moderation.

PeGraph: A System for Privacy-Preserving and Efficient Search Over Encrypted Social Graphs

With the widespread adoption of cloud computing, it is increasingly popular for online social network (OSN) service providers to leverage the public cloud as a back-end to manage their services for the cloud’s well-understood benefits. However, the cloud is also notoriously subject to a wide attack surface, making it an imperative need to embed security in the cloud-backed OSN service from the very beginning. In light of this, in this paper, we design, implement, and evaluate PeGraph, the first system simultaneously allowing private, efficient, and rich queries over encrypted social graphs. PeGraph is aimed at safeguarding the confidentiality of the social graph at the cloud, while preserving the functionality of social search, a key enabler for quality OSN services like friend discovery and user targeting. PeGraph is built from a delicate synergy of insights from social graph modelling and lightweight cryptography such as searchable encryption and additive secret sharing, supporting rich social search queries like exact queries, fuzzy queries, and mixed queries. PeGraph also allows the cloud to obliviously render the encrypted social search results in a ranked order according to their importance, as per users’ preferences. Extensive experiments demonstrate that PeGraph can securely process a wide range of practical social search queries within 1 second, over a real-world social graph consisting of millions of entities.

∊-k anonymization and adversarial training of graph neural networks for privacy preservation in social networks

With the explosive growth of social networks, privacy preservation as a social good has been one common concern. Graph neural networks (GNNs) have been utilized by social network service providers to improve business service. However, traditional anonymization techniques of social networks cannot satisfy the desired privacy preservation of node attribute and graph structure and introduce information disturbance from the anonymization, leading to the performance degradation of GNNs in social network analysis. To protect sensitive user data and persist GNNs’ performance in social network analysis, we propose a two-stage privacy-preserving method of graph neural networks in the social network domain. During the first stage, we design a novel ∊-k anonymization method that can achieve ∊-local differential privacy (∊-LDP) and k-degree anonymity by incorporating the classical LDP and k-degree anonymization (k-DA) while retaining as much network community information as possible. At the second stage, we develop an adversarial training mechanism for GNNs to resist the disturbance from ∊-k anonymization and retain as much task performance as possible on anonymous social network data. Comprehensive experiments on several real-world social network datasets demonstrate the effectiveness of the proposed method for privacy-preserving node classification, link prediction, and graph clustering in social networks. The proposed method represents an interesting and important combination of classical anonymous technologies and recent GNNs and can preserve user privacy while providing business service.

An authorized equality test on identity‐based cryptosystem for mobile social networking applications

AbstractThe widespread adoption of social network applications and smart mobile devices has yielded a massive amount of data transmitted through the networks. Fortunately, social network service providers offer a wide range of storage capacity and data management functionality. Accordingly, the data can be uploaded to the social servers for processing and then accessible elsewhere. Nevertheless, considering the untrusted nature of the social servers, it becomes crucial to protect the data before uploading it to the server. Unfortunately, encryption incurs another severe problem in providing search functionality on encrypted data stored on the server. In addition, most of the existing solutions are inefficient to deploy on mobile devices due to the limited resources and computing capabilities. To address this issue, we propose an authorized equality test on identity‐based cryptosystem (AET‐IBC) scheme in this article. The scheme allows the server to test if two different ciphertexts encapsulate the same message. Besides, it supports different authorization methods to enhance the privacy. Furthermore, based on the random oracle model, we prove the security of the proposed scheme to be equivalent to the modified bilinear Diffie‐Hellman intractable assumption. Finally, the efficiency analysis shows that the AET‐IBC scheme is practicable and convenient for mobile social networking applications.

CONFIDENTIALITY PROTECTIVE USER PROFILE MATCHING IN PUBLIC NETWORKS

We consider a scenario where a user queries a user profile database, maintained by a social networking service provider, to identify users whose profiles match the profile specified by the querying user. A typical example of this application is online dating. Most recently, an online dating website, Ashley Madison, was hacked, which results in disclosure of a large number of dating user profiles. This data breach has urged researchers to explore practical privacy protection for user profiles in a social network. Here, we propose a privacy-preserving solution for profile matching in social networks by using multiple servers. Our solution is built on homomorphic encryption and allows a user to find out matching users with the help of multiple servers without revealing to anyone the query and the queried user profiles in clear. Our solution achieves user profile privacy and user query privacy as long as at least one of the multiple servers is honest. Our experiments demonstrate that our solution is practical. KEY WORDS: User profile matching, data privacy protection, ElGamal encryption.

Power of Earned Advertising on Social Network Services: A Case Study of Friend Tagging on Facebook

Social network services (SNSs) are now the primary advertisingmedium in terms of both reach and engagement.For both businesses and the SNS providers, it iscrucial to find advertising methods that users perceiveto be valuable. In this paper, we provide an empiricalevidence for the role of different advertising methodson SNSs (i.e. earned vs. paid) on the subjective evaluationof the relative worth of advertising. In particular,we concentrate on the act of ‘friend tagging’ – the conventionof tagging friends in a thread to a brand post –where users inadvertently engage in targeted and personalizedbrand advertising. Through survey analysis,we validate that users find earned advertising less irritatingand more informative, entertaining, and crediblethan paid advertising. We further ask if brands canstrategically craft their content to boost up friend tagging.Using the data collected from Facebook, we analyzewhat drives users to engage in friend tagging andfind that content characteristics such as media attachmentsand posting times affect friend tagging frequency.We conclude that friend tagging is a powerful userinitiatedsolution for matching products with potentialtarget audience.

Anonymization Techniques for Privacy Preserving Data Publishing: A Comprehensive Survey

Anonymization is a practical solution for preserving user’s privacy in data publishing. Data owners such as hospitals, banks, social network (SN) service providers, and insurance companies anonymize their user’s data before publishing it to protect the privacy of users whereas anonymous data remains useful for legitimate information consumers. Many anonymization models, algorithms, frameworks, and prototypes have been proposed/developed for privacy preserving data publishing (PPDP). These models/algorithms anonymize users’ data which is mainly in the form of tables or graphs depending upon the data owners. It is of paramount importance to provide good perspectives of the whole information privacy area involving both tabular and SN data, and recent anonymization researches. In this paper, we presents a comprehensive survey about SN (i.e., graphs) and relational (i.e., tabular) data anonymization techniques used in the PPDP. We systematically categorize the existing anonymization techniques into relational and structural anonymization, and present an up to date thorough review on existing anonymization techniques and metrics used for their evaluation. Our aim is to provide deeper insights about the PPDP problem involving both graphs and tabular data, possible attacks that can be launched on the sanitized published data, different actors involved in the anonymization scenario, and major differences in amount of private information contained in graphs and relational data, respectively. We present various representative anonymization methods that have been proposed to solve privacy problems in application-specific scenarios of the SNs. Furthermore, we highlight the user’s re-identification methods used by malevolent adversaries to re-identify people uniquely from the privacy preserved published data. Additionally, we discuss the challenges of anonymizing both graphs and tabular data, and elaborate promising research directions. To the best of our knowledge, this is the first work to systematically cover recent PPDP techniques involving both SN and relational data, and it provides a solid foundation for future studies in the PPDP field.

Privacy-Preserving User Profile Matching in Social Networks

In this paper, we consider a scenario where a user queries a user profile database, maintained by a social networking service provider, to identify users whose profiles match the profile specified by the querying user. A typical example of this application is online dating. Most recently, an online dating website, Ashley Madison, was hacked, which resulted in a disclosure of a large number of dating user profiles. This data breach has urged researchers to explore practical privacy protection for user profiles in a social network. In this paper, we propose a privacy-preserving solution for profile matching in social networks by using multiple servers. Our solution is built on homomorphic encryption and allows a user to find out matching users with the help of multiple servers without revealing to anyone the query and the queried user profiles in clear. Our solution achieves user profile privacy and user query privacy as long as at least one of the multiple servers is honest. Our experiments demonstrate that our solution is practical.

Gift giving via social network services: the case of a WeChat mini-program used in China

PurposeThe purpose of this paper is to examine and explore the factors that drive users to gift through social network services (SNS).Design/methodology/approachA questionnaire method was applied to collect data from the sample of the WeChat users who have used mini-program. This paper employed the partial least squares method and used SmartPLS2.0 to analysis sample data, which examined the validity as well as reliability of the sample and further tested the hypotheses by the path coefficients.FindingsThe empirical results showed that pleasure, social relationship maintenance, convenience and comprehensiveness are significantly related to SNS gifting behavior, and conscientiousness moderates the relationship between intention and behavior in the context of SNS gifting. However, this study cannot find the effect of symbolic representation, impersonality and gift reciprocity motivations.Research limitations/implicationsTheoretically, this study perfects the research of SNS gifting on the lack of exploring characteristics of comprehensiveness. Practically, this paper lends insights on how SNS providers attract users to adopt gifting.Originality/valueSNS gifting lacks a complete and effective promotion strategy, resulting in a small number of users as well as low profit. Besides, prior studies have focused on tradition gifting and online gifting. Little research talks about gifting on SNS phenomena, and thus it is necessary to perfect the theory of SNS gifting.

How Government Surveillance Policies Modify SNS Use in South Korea

Abstract The present study examines the assumption that online users would be influenced by widely reported privacy threats; namely, that public servants might monitor personal online communications between instant messenger users, and compel the Korean domestic social network service (SNS) providers to cooperate with their surveillance efforts. Utilizing uses and gratifications (U&G) theory, we integrate previous research on government surveillance, privacy concern, and motivation variables regarding SNS use. A survey of South Korean users reveals that privacy concern is mediating the relationship between governmental online surveillance and SNS switching intention. Structural equation modeling results suggest that SNS switching intention is predicted positively by interaction motivation and negatively by convenience motivation. Privacy concern mediates the relationship between governmental online surveillance and SNS switching intention. These findings illustrate the measures that users take in response to telecommunication policy actions, particularly those that might logically pose a threat to online privacy. Study findings thus help provide support for a novel theoretical framework that illustrates the utility of media U/G variables in the context of online privacy conceptions stemming from perceived threats of online government surveillance. We conclude by discussing implications for policymakers stemming from user remedies to circumvent state surveillance initiatives.

How Government Surveillance Policies Modify SNS Use in South Korea

Abstract The present study examines the assumption that online users would be influenced by widely reported privacy threats; namely, that public servants might monitor personal online communications between instant messenger users, and compel the Korean domestic social network service (SNS) providers to cooperate with their surveillance efforts. Utilizing uses and gratifications (U&G) theory, we integrate previous research on government surveillance, privacy concern, and motivation variables regarding SNS use. A survey of South Korean users reveals that privacy concern is mediating the relationship between governmental online surveillance and SNS switching intention. Structural equation modeling results suggest that SNS switching intention is predicted positively by interaction motivation and negatively by convenience motivation. Privacy concern mediates the relationship between governmental online surveillance and SNS switching intention. These findings illustrate the measures that users take in response to telecommunication policy actions, particularly those that might logically pose a threat to online privacy. Study findings thus help provide support for a novel theoretical framework that illustrates the utility of media U/G variables in the context of online privacy conceptions stemming from perceived threats of online government surveillance. We conclude by discussing implications for policymakers stemming from user remedies to circumvent state surveillance initiatives.

Topic-Based Influence Computation in Social Networks Under Resource Constraints

As social networks are constantly changing and evolving, methods to analyze dynamic social networks are becoming more important in understanding social trends. However, due to the restrictions imposed by the social network service providers, the resources available to fetch the entire contents of a social network are typically very limited. As a result, analysis of dynamic social network data requires maintaining an approximate copy of the social network for each time period, locally. In this paper, we study the problem of dynamic network and text fetching with limited probing capacities, for identifying and maintaining influential users as the social network evolves. We propose an algorithm to probe the relationships (required for global influence computation) as well as posts (required for topic-based influence computation) of a limited number of users during each probing period, based on the influence trends and activities of the users. We infer the current network based on the newly probed user data and the last known version of the network maintained locally. Additionally, we propose to use link prediction methods to further increase the accuracy of our network inference. We employ PageRank as the metric for influence computation. We illustrate how the proposed solution maintains accurate PageRank scores for computing global influence, and topic-sensitive weighted PageRank scores for topic-based influence. The latter relies on a topic-based network constructed via weights determined by semantic analysis of posts and their sharing statistics. We evaluate the effectiveness of our algorithms by comparing them with the true influence scores of the full and up-to-date version of the network, using data from the micro-blogging service Twitter. Results show that our techniques significantly outperform baseline methods and are superior to state-of-the-art techniques from the literature.