Privacy In Social Networks Research Articles

Exploring Temporal Evaluations of Interpersonal Social Media Surveillance During the COVID-19 Lockdown

The occurrence of the novel coronavirus necessitates a better understanding of how romantic partners use social technology to cope with health stressors. This exploratory study, therefore, examined whether COVID-related health concerns regarding oneself or one’s romantic partner before/during quarantine predict, or are predicted by, emerging adults’ engagement in social media surveillance of their romantic partner. Participants ( N = 181 emerging adults in a romantic relationship) responded to online surveys at two points during spring 2020. Findings from a cross-lagged analysis indicate that COVID-related health concerns for oneself before stay-at-home orders predicted emerging adult’s participation in social media surveillance of a romantic partner during COVID quarantine. This study serves as an initial inquiry into how health-related concerns impact technology use in romantic relationships and how they serve to modify digital participation during a global crisis (i.e., the COVID-19 pandemic). Limitations, future research directions, and implications of the study are discussed.

Vaccine Hesitancy in Parents: Role of Social Networks, Social Media, and Parental Autonomy

Introduction. Vaccine hesitancy in parents is a composite of multiple dimensions such as confidence, complacency, and convenience. A large proportion of parents can be deemed vaccine hesitant, meaning that their vaccine behaviors can range from delaying vaccines, skipping select vaccines, to refusal of all vaccinations. Furthermore, parental vaccine uptake rates and patterns can reflect their decisions based on the balance of parental autonomy versus protecting population health. The current manuscript uses Social Network Theory to explain some of the external influences on parental autonomy. Social networks, both private and public, play a role in vaccine decision making through providing information and support for parents in their choices. This influence, in turn, is mediated by parents’ health literacy and local vaccination policy. Discussion. Social media is an important type of public network that has an outsized influence on vaccine hesitancy. The rhetoric used on anti-vaccine websites often denigrates scientific evidence while at the same time endorsing poor-quality evidence that supports the anti-vaccine point of view. The websites continually propose new hypotheses of how vaccines can cause harm when studies refute their previous assertions, censor critics, and attack people with opposing viewpoints. The contentious nature of vaccine hesitancy based on beliefs, opinions, and attitudes needs a solution much deeper than simply providing factual knowledge or pointing people to reliable websites. Recommendations. Public health practitioners and researchers should try segmenting audiences, targeting private and public social networks, and then testing which persuasive strategies towards vaccinations appeal to different community groups.

Design and Implementation of Private Social Network

Private social networking, is a web-based networking platform which is used in a particular Institution, schools, colleges or organizations (In this current project we are implementing it for college institution purpose only). In this social network people can communicate securely, since it’s a private social network and it’s not free, each & every student must pay the amount decided by the institution. It’s not compulsory to join this social network it’s the student wish. If he need this social network then the admin will provide full support to the student to activate the account. In this network students can communicate with their teachers in order to get help in studies. Even the teachers can also update latest news about technology, politics etc. And the time table, circular information for students and for college can be updated by teachers or institution, so that message can be passed to every student easily. In this network student can upload images, document files. This private network is highly anti-threatened towards social disturbing activities, so that there’ll be no disturbances in the institution. All the User Accounts created in this Social network are valid only for Limited time. In this project all the data will be accepted through .JSP and .PHP scripts which is our front end. All the data will be managed by the database. MySQL which is our back end.

Large-Scale Social Network Privacy Protection Method for Protecting K-Core

Social network analysis has many important applications and methods which depend on the sharing and publishing of graphs. For example, link privacy requires limiting the probability of an adversary identifying a target sensitive link between two individuals in the published social network graph. However, the existing link privacy protection methods have low processing power for large-scale graph data and less consideration of community protection in the publishing graphs. Therefore, aiming at sensitive link privacy protection, a large-scale social network privacy protection model to protect K-Core (PPMPK) was proposed. The large-scale social network graph was processed to ensure that the core number and the community structure of the nodes were unchanged based on the Pregel parallel graph processing model. Extensive experiments on the real data sets showed that the proposed method could effectively process the large-scale graph data and protect the data availability of the published graphs, especially in community protection.

Surveillance Concerns as Predictors of Obfuscation and the Chilling Effect in the Context of a Pandemic

Potential negative consequences of digital surveillance represent an area of increasing concern due to the rising impact of digital and mobile technologies on daily life. The COVID-19 pandemic increased these concerns as governments worldwide turned to both digital and non-digital surveillance to help in the battle to control the spread of the disease. Due to this, surveillance creep (the use of supposedly limited-scope surveillance for increasingly pervasive purposes) is a growing concern. Concerns over digital surveillance have led to some individuals turning to protective measures, including obfuscation and the chilling effect. Obfuscation involves intentionally providing ambiguous or misleading information to interfere with surveillance activities. The chilling effect is the decision to not engage in some behavior due to concerns about the consequences of that behavior. Often, obfuscation is a general protective measure that guards against surveillance across applications and reflects general concerns about privacy and surveillance. In contrast, the chilling effect is application and concern specific. In this paper, we use a research model that draws on the health beliefs model and protection motivation along with data from a survey of American social media users to investigate antecedents of obfuscation and the chilling effect in the context of social media surveillance related to COVID-19. Results indicate that age, sex, social media experience, social media habit, and the perceived surveillance severity impact obfuscation. These same antecedents affect the chilling effect, as does perceived surveillance vulnerability. Although our research is exploratory, the results of our study hold implications for both research and practice.

Hate Speech Detection in Social Media Surveillance: A Review of Related Literature

Social media surveillance is a requirement for governments and intelligence agencies around the world to detect and prevent hate crimes. The dynamic and unstructured nature of the textual content available on social media platforms makes it very complex to extract hate related speech patterns from this content. It also creates ambiguities in the data and therefore, data mining techniques become difficult to apply in this scenario. Several alternative techniques were adopted by different researchers in the past to cope with this problem and to capture and analyze such unstructured text for the purpose of hate speech detection. In this paper, we reviewed, categorized and presented a state-of-the-art of these techniques which were divided in to three categories namely text mining, sentiment analysis and semantics. The challenges in the application of the existing techniques were also discussed and these can be taken up as future directions

Privacy-Enhancing k-Nearest Neighbors Search over Mobile Social Networks.

Focusing on the diversified demands of location privacy in mobile social networks (MSNs), we propose a privacy-enhancing k-nearest neighbors search scheme over MSNs. First, we construct a dual-server architecture that incorporates location privacy and fine-grained access control. Under the above architecture, we design a lightweight location encryption algorithm to achieve a minimal cost to the user. We also propose a location re-encryption protocol and an encrypted location search protocol based on secure multi-party computation and homomorphic encryption mechanism, which achieve accurate and secure k-nearest friends retrieval. Moreover, to satisfy fine-grained access control requirements, we propose a dynamic friends management mechanism based on public-key broadcast encryption. It enables users to grant/revoke others’ search right without updating their friends’ keys, realizing constant-time authentication. Security analysis shows that the proposed scheme satisfies adaptive -semantic security and revocation security under a random oracle model. In terms of performance, compared with the related works with single server architecture, the proposed scheme reduces the leakage of the location information, search pattern and the user–server communication cost. Our results show that a decentralized and end-to-end encrypted k-nearest neighbors search over MSNs is not only possible in theory, but also feasible in real-world MSNs collaboration deployment with resource-constrained mobile devices and highly iterative location update demands.

VARTOTOJŲ ELGESIO INTERNETE ANALIZĖ

In most cases the internet has a positive effect on its users – it offers unrestrained communication, expression of opinions and sense of openness. On the other hand, excessive usage leads to internet addiction. The most important features of the internet include rapid spread of information and news, access to tremendous amount of information, personalization and anonymity. After the literature review of theoretical research of authors from Lithuania and abroad, and application of theoretical analytical, resumptive, analogical, deductive and comparative methods, the features of XXI century media literacy were identified. A questionnaire allowed to examine the internet’s specifics and nature of users’ media literacy online. Empirical research was conducted to analyse the internet users’ behavior based on age groups as well as to identify their level of literacy. A big part of today’s daily activity and free time is related to usage of the internet media. Many services take an online form. Therefore, the issue of Lithuanian users’ online behavior becomes a concurrent to the development of welfare society, and media literacy gets to be one of the necessities for future skills. Instead of searching for and passively using the given information, internet users start to make their own, evaluate it and spread further. The users are enabled to fashion their private social networks, they become responsible for organization of the whole personal communication infrastructure. This research points out the main reasons of Lithuanian internet users not using e-services: the lack of media literacy and insufficient spread of the service-related information. The biggest difference in literacy among age groups was noticed while evaluating personal predisposition to internet addiction. Levels of literacy based on age groups were significantly different in terms of ability to critically evaluate online information. KEY WORDS: internet, media, media literacy, internet user’s behavior, welfare society. DOI: http://dx.doi.org/10.15181/tbb.v71i2.1102

Evaluating security and privacy issues of social networks based information systems in Industry 4.0

ABSTRACT The present study aimed to analyse the main risks related to security and privacy of social networks based information systems in Industry 4.0. The methodology we used is an innovative exploratory data-driven process divided into three steps. First, we performed sentiment analysis to divide the database composed of 67, 206 tweets into feelings. Second, we applied a topic-modelling algorithm to extract topics. Third, we applied textual analysis to collect insights. A total of 10 topics related to security and privacy issues were identified as results. The paper concludes with a discussion of the challenges and main concerns related to the identified topics.

How Not to Measure Social Network Privacy

Privacy has been conceptualized as a multi-dimensional construct in prior research. However, most multi-dimensional conceptualizations were developed based on populations from western countries. It remains an open question whether the underlying dimensions of privacy stays consistent in non-western countries. Through a series of factor analyses on two survey datasets, we compare the dimensions of privacy concern, information disclosure, general disclosiveness, and privacy management strategies among social network users in the US, China and South Korea. We find significant cross-country differences in the dimensions of these privacy-related concepts, indicating that the fundamental understanding of these concepts varies substantially across these countries. We discuss possible explanations of these cross-country differences and make methodological suggestions for future work.

Survey of community privacy in social network

Survey of community privacy in social network

Interplay of Support, Comparison, and Surveillance in Social Media Weight Management Interventions: Qualitative Study.

BackgroundThere has been a significant increase in the trend of using social media as a platform to deliver weight management interventions. This illustrates a need to develop a holistic understanding of doctor-patient communication and peer-to-peer communication in social media interventions and to determine their influences on weight management for people with overweight or obesity. Such studies will highlight how social media can be more effectively integrated into weight management programs to enhance individuals’ short-term and long-term weight management behaviors.ObjectiveThe aim of this study was to examine patients’ experiences with doctor-patient communication and peer interactions in a social media–based (WeChat) weight management program, and to describe the interplay of three social influence factors—social support, social comparison, and surveillance—in their weight control practices. The program, designed and implemented by the research team located in a tertiary referral hospital in a southeastern province in China, included both diet and physical activity components that targeted people with overweight or obesity.MethodsWe conducted in-depth interviews with 32 program participants of different ages (mean 35.6, SD 7.7 years), gender (18 women), duration of program membership (mean 1.4 years), and weight loss outcomes (54% weight loss to 9% weight gain). All interview data were audio-recorded, transcribed, and translated using the translation-backtranslation technique. Nvivo software was used to facilitate the coding process.ResultsResults of thematic analysis indicated the distinct functions of professionally led support and peer support. Professional support was presented in the form of knowledge infusion, efficacy enhancement, and provision of timely feedback. Peer support fostered empathy and sense of belonging, and had a mutually reinforcing relationship with peer comparison and peer-based surveillance. Peer comparison enhanced motivation and positive competition. However, it also reinforced negative group norms, and resulted in downturns in reference standards and collective inactivity. Social media surveillance prompted participants’ reactions to the gaze from medical professionals and peers that could be encouraging or inhibiting. Surveillance enhanced vigilance with weight control norms; however, its influence weakened when participants chose to fake weight data and turn off notifications. Findings from this study illustrated the interrelated and fluctuating influences of support, comparison, and surveillance.ConclusionsThe interactive traits of social media eased the practices of social support and social comparison, and created new forms of surveillance. This study contributes to an in-depth understanding of social media influences on individuals’ weight control behaviors. Practical implications of the study concern improved strategies for maintaining the positive dynamics of social media interactions and preventing negative resistance to surveillance technology.Trial RegistrationChinese Clinical Trial Registry ChiCTR1900025861; http://www.chictr.org.cn/showprojen.aspx?proj=42497

A New Era in Pharmacovigilance: Toward Real-World Data and Digital Monitoring.

Adverse drug reactions (ADRs) are a major concern for patients, clinicians, and regulatory agencies. The discovery of serious ADRs leading to substantial morbidity and mortality has resulted in mandatory phase IV clinical trials, black box warnings, and withdrawal of drugs from the market. Real‐world data, data collected during routine clinical care, is being adopted by innovators, regulators, payors, and providers to inform decision making throughout the product life cycle. We outline several different approaches to modern pharmacovigilance, including spontaneous reporting databases, electronic health record monitoring and research frameworks, social media surveillance, and the use of digital devices. Some of these platforms are well‐established while others are still emerging or experimental. We highlight both the potential opportunity, as well as the existing challenges within these pharmacovigilance systems that have already begun to impact the drug development process, as well as the landscape of postmarket drug safety monitoring. Further research and investment into different and complementary pharmacovigilance systems is needed to ensure the continued safety of pharmacotherapy.

Aggregation Encryption Method of Social Network Privacy Data Based on Matrix Decomposition Algorithm

Currently, there are some defects in data aggregation encryption methods, such as low accuracy and efficiency. In this regard, this paper proposes an aggregation encryption method of social network privacy data based on matrix decomposition. Shamir threshold segmentation scheme was used to improve the security of matrix decomposition results, and then re-encrypted network user attributes. For codomain and non-codomain users, private file and key generation technology were used to improve the privacy of users in common domain and non-common domain. Considering the results of matrix decomposition, privacy security and efficient user matching in social networks were implemented. The node information was embedded in the process of data encryption and signature, and the interference factors were classified by cluster as a unit, which can resist the internal attack and improve the operation efficiency. The experimental results show that this method has excellent encryption effect and high privacy data protection processing efficiency.

The Case of the Nosy Neighbors

Inspired by companies like Clearview AI, Nextdoor, and Amazon, this case study asks students to assume the role of a high-ranking ethics-focused employee at a (fictional) neighborhood-focused social media company. It involves challenging ethical questions around how social media services and surveillance tools are built and used, and the complicated relationship between companies, their users, and law enforcement authorities. Students should pay particular attention to the values implicated by certain design decisions, and the competing incentives for corporations that might complicate the picture for ethical decision making.

Privacy Inference Attack Against Users in Online Social Networks: A Literature Review

With the rapid development of social networks, users pay more and more attention to the protection of personal information. However, the transmission of users’ personal information through social networks will inevitably lead to privacy leakage and make users attacked. A large amount of privacy information can be inferred from the content and social traces published by users, which leads to the rise of privacy inference technology for users in social networks. Social relationship inference and attribute inference are two basic attacks on users’ privacy in social networks. This is the first systematic review of privacy inference attacks in social networks. The purpose of this retrospective study is to provide a global summary, summarizing the time trend of the topic, and show the evolution of the topic in the past 15 years. In addition, this paper discusses the trend and development of this topic and finally looks forward to future work. The contribution of our work is helpful for researchers to continue their research in this field.

Trace Me If You Can: An Unlinkability Approach for Privacy-Preserving in Social Networks

Privacy in social networks has been a vast active area of research due to the enormous increase in privacy concerns with social networking services. Social networks contain sensitive information of individuals, which could be leaked due to insecure data sharing. To enable a secure social network data publication, several privacy schemes were proposed and built upon the anonymity of users. In this paper, we incorporate unlinkability in the context of weighted network data publication, which has not been addressed in prior work. Two key privacy models are defined, namely <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">edge weight unlinkability</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">node unlinkability</i> to obviate the linking of auxiliary information to a targeted individual with high probability. Two new schemes satisfying these unlinkability notions, namely <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MinSwap</i> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\delta $ </tex-math></inline-formula> <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-MinSwapX</i> are proposed to address edge weight disclosure, link disclosure and identity disclosure problems in publishing weighted network data. The edge weight is modified based on minimum value change in order to preserve the usefulness and properties of the edge weight data. In addition, edge randomization is performed to minimally modify the structural information of a user. Experimental results on real data sets show that our schemes efficiently achieve data utility preservation and privacy protection simultaneously.

Enhancing the Privacy Protection in Social Networks

This paper aims to design an application to find solutions for protecting the privacy of social networks’ big data by using the artificial neural networks (ANN). In particular, the researcher designs a new algorithm for training an ANN to detect the fake accounts of Twitter according to some conditions to protect the user from the external attacks by the hackers. The program was written in (JAVA) language to train ANN by collecting information about each account which supported by Twitter4j. The researchers concluded many results, the main conclusions of the paper were that any Twitter account will have sensitive to the fake account by applying our program. So the fake account will not be able to exploit the user. Fake accounts detection can be complement intrusion prevention techniques to increase the SNS security. The proposed ANN should be done locally in each connect in the SNS. Moreover, attacks detection should get the place in all SNS types.

Association between women's level of education and family daily nutrient intake and obesity status in Iraq

Background: Mothers play an important role in controlling family eating habits as well as the type of food consumed. Obesity is one of the causing factors for many diseases. Objectives: This study was designed to determine whether a woman's level of education is associated with the family's daily dietary intake and mother weight status or not. Materials and Methods: A web survey study designed through Google Forms was sent online to the entire Iraqi population from August 26 to September 20, 2020. The survey was distributed through institutional and private social networks (University E-mail, Facebook, Instagram, Viber, and WhatsApp). This method was entirely effective for the research hypothesis. A face-to-face interview was also conducted with illiterate women in public hospitals and medical centers. Results: The study result by 376 participating women showed a significant relationship between the level of education and body mass index (BMI) (P = 0.001). No significant relationship was shown between the level of daily dietary intake and BMI. The results also showed a significant association between education and family level of dietary intake (P = 0.003). The study showed a better daily dietary intake with those who do not have a high education level. Conclusions: The study concluded that healthy dietary choices and daily food intake are not related to education level but the mother's nutritional knowledge level.

Opinions of healthcare leaders on the barriers and challenges of using social media in Saudi Arabian healthcare settings

Abstract Background Several authors have studied the benefits and risks of using social media in healthcare settings. However, there are few studies on the opinion of healthcare leaders to face the challenges of using social media networks. Purpose The objective of this study was to investigate the opinions of healthcare leaders on the barriers and challenges of using social media in Saudi Arabian healthcare settings. Method ology: A cross-sectional study was conducted to collect information related to the research objective. A questionnaire distributed through Telegram, Twitter, and WhatsApp was used for this purpose. Initially, the questionnaire was sent to 226 healthcare leaders and 70 participants completed and returned the questionnaire. The data were analyzed using basic statistics. Results More than half of the respondents (84.3%) used social media and WhatsApp was the most used platform (78.6%). Most of the participants (90%) believed that social media must be used in healthcare settings and 78.6% of them thought that there were barriers to use social media in healthcare scenery. Some of the detected barriers were the absence of clear policies, ethical reasons, security issues, dissemination of inaccurate information, and so forth. The main suggestions to face the challenges generated by these barriers were to implement security mechanisms to guarantee the privacy and confidentiality of patient data, establish websites and social networks managed by certified specialists, and monitor the information posted on social media to ensure that it is reliable. Also, the participants considered that it was necessary to implement clear guidelines and policies on how to use social media adequately. They also thought that the Ministry of Health Education should observe the information published on social networks. Conclusion The majority of respondents agreed on the importance of using social media in healthcare settings in Saudi Arabia. However, the participating healthcare leaders considered that there were barriers and challenges related to the use of these platforms. In this sense, they suggested a set of solutions to use social networks efficiently and safely to improve the healthcare system in the Kingdom of Saudi Arabia.