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The Web is the largest repository of information. Personal information is usually scattered on various pages of different websites. Search engines have made it easier to find personal information. An attacker may collect a user's scattered information together via search engines, and infer some privacy information. The authors call this kind of privacy attack “Privacy Inference Attack via Search Engines”. The purpose of this paper is to provide a user‐side automatic detection service for detecting the privacy leakage before publishing personal information.

In this paper, the authors propose a user‐side automatic detection service. In the user‐side service, the authors construct a user information correlation (UICA) graph to model the association between user information returned by search engines. The privacy inference attack is mapped into a decision problem of searching a privacy inferring path with the maximal probability in the UICA graph and it is proved that it is a nondeterministic polynomial time (NP)‐complete problem by a two‐step reduction. A Privacy Leakage Detection Probability (PLD‐Probability) algorithm is proposed to find the privacy inferring path: it combines two significant factors which can influence the vertexes' probability in the UICA graph and uses greedy algorithm to find the privacy inferring path.

The authors reveal that privacy inferring attack via search engines is very serious in real life. In this paper, a user‐side automatic detection service is proposed to detect the risk of privacy inferring. The authors make three kinds of experiments to evaluate the seriousness of privacy leakage problem and the performance of methods proposed in this paper. The results show that the algorithm for the service is reasonable and effective.

The paper introduces a new family of privacy attacks on the Web: privacy inferring attack via search engines and presents a privacy inferring model to describe the process and principles of personal privacy inferring attack via search engines. A user‐side automatic detection service is proposed to detect the privacy inference before publishing personal information. In this user‐side service, the authors propose a Privacy Leakage Detection Probability (PLD‐Probability) algorithm. Extensive experiments show these methods are reasonable and effective.

The purpose of this study is to explore the effects of recommendation algorithm, product reputation, new product novelty, privacy concern and privacy protection behavior on users’ satisfaction and continuance intention to use short-form video application (APP).

Based on the existing theories, the research model of this study was developed and 445 valid data were collected through a questionnaire survey. The partial least squares structural equation modeling (PLS-SEM) was employed for data analysis to test the research model and hypotheses.

The results reveal that the recommendation algorithm has a significant positive effect on user satisfaction, new product novelty and privacy concern. The influence of recommendation algorithm on privacy concern is negatively moderated by product reputation. Privacy concern has a significant and positive impact on privacy protection behavior, and privacy protection behavior has a significant and positive impact on user satisfaction. New product novelty also has significant impact on user satisfaction.

This study is one of the earliest studies to incorporate recommendation algorithm as a construct into the college students’ continuance intention to use short-form video APP. The influence of reputation as a moderator variable on the relationship between algorithm and privacy concerns is also investigated.

The paper aims at a multi‐faceted review of scholarly work, analyzing the current state of empirical studies dealing with privacy and online social networking (OSN) as well as the theoretical “puzzle” of privacy approaches related to OSN usage from the background of diverse disciplines. Drawing on a more pragmatic and practical level, aspects of privacy management are presented as well.

Based on individual privacy concerns and also publicly communicated threats, information privacy has become an important topic of public and scholarly discussion. Beside diverse positive aspects of OSN sites for users, their information is for example also being used for data mining and profiling, pre‐recruiting information as well as economic espionage. This review highlights information privacy mainly from an individual point‐of‐view, focusing on the usage of OSN sites (OSNs).

This analysis of scholarly work shows the following findings: first, adults seem to be more concerned about potential privacy threats than younger users; second, policy makers should be alarmed by a large part of users who underestimate risks of their information privacy on OSNs; third, in the case of using OSNs and its services, traditional one‐dimensional privacy approaches fall short. Hence, findings of this paper further highlight the necessity to focus on multidimensional and multidisciplinary frameworks of privacy, for example considering a so‐called “privacy calculus paradigm” and rethinking “fair information practices” from a more and more ubiquitous environment of OSNs.

The results of the work presented in this paper give new opportunities for research as well as suggestions for privacy management issues for OSN providers and users.

The paper aims to resolve three major issues in location-based applications (LBA) known as heterogeneity, user privacy, and context-awareness by proposing an elastic and open design platform named OpenLS privacy-aware middleware (OPM) for LBA.

The paper analyzes relevant approaches ranging from both academia and mobile industry community and insists the importance of heterogeneity, user privacy, and context-awareness towards the development of LBA.

The paper proposes the OPM by design. As a result, the OPM consists of two main component named application middleware and location middleware, which are cooperatively functioned to achieve the above goals. In addition, the paper has given the implementation of the OPM as well as its experiments. It is noted that two privacy-preserving techniques at two different levels are integrated into the OPM, including Memorizing algorithm at the application level and Bob-tree at the database level. Last but not least, the paper shows further discussion about other problems and improvements that might be needed for the OPM.

Each issue has its sub problems that cause more influences to the OPM. Besides, each of the issues requires more investigations in depth in order to have better solutions in detail. Therefore, more overall experiments should be conducted to assure the OPM's scalability and effectiveness.

The paper hopefully promotes and speeds up the development of LBA when providing the OPM with suitable application programming interfaces and conforming the OpenLS standard.

This paper shows its originality towards location-based service (LBS) providers to develop their applications and proposes the OPM as a unified solution dealing with heterogeneity, user privacy, and context-awareness in the world of LBS.

Online privacy research has seen a focus on user behavior over the last decade, partly to understand and explain user decision-making and seeming inconsistencies regarding users' stated preferences. This article investigates the level of modeling that contemporary approaches rely on to explain said inconsistencies and whether drawn conclusions are justified by the applied modeling methodology. Additionally, it provides resources for researchers interested in using computational modeling.

The article uses data from a pre-existing literature review on the privacy paradox (N = 179 articles) to identify three characteristics of prior research: (1) the frequency of references to computational-level theories of human decision-making and perception in the literature, (2) the frequency of interpretations of human decision-making based on computational-level theories, and (3) the frequency of actual computational-level modeling implementations.

After excluding unrelated articles, 44.1 percent of investigated articles reference at least one theory that has been traditionally interpreted on a computational level. 33.1 percent of all relevant articles make statements regarding computational properties of human cognition in online privacy scenarios. Meanwhile, 5.1 percent of all relevant articles apply formalized computational-level modeling to substantiate their claims.

The findings highlight the importance of formal, computational-level modeling in online privacy research, which has so far drawn computational-level conclusions without utilizing appropriate modeling techniques. Furthermore, this article provides an overview of said modeling techniques and their benefits to researchers, as well as references for model theories and resources for practical implementation.

The adoption of social media has been extensively discussed. However, to explain the adoption of traditional social media, considering the benefits and risks accumulated from the experiences of social media use, the extent literature is limited. Thus, this paper investigated the act of traditional social media users’ switching behavior from a dynamic perspective and the level of information privacy concerns and social media privacy to measure the risks and benefit accumulated from this dynamic process.

This study of Facebook and Twitter users, who are regarded as representative of traditional social media, are selected as research targets surveyed and were required to answer a specially designed questionnaire in order to determine their general feeling on social media platforms they currently use. As a part of this process, quota sampling was used to collect different samples based on gender and age. In this paper, t-test, one-way ANOVA and multiple comparisons were used for the statistical analysis, conducted through SPSS.

Information privacy concerns and social media dependency affect the adoption of social media. Secondly, social media dependency is a more salient determinant for social media adoption. Therefore, social media firms should pay more attention to enhancing user dependency of social media by increasing user involvement of social media.

This study intends to conduct a research design that provides an overall and holistic understanding of user usage experience. To do this, it investigates the intensity of switching behavior through the level of dependency and the level of information privacy concern that users inevitably exhibit through the use of social media over long time.

Push notification service (PNS) is an important approach to distribute personalized information to users timely and is getting more and more popular. However, users' privacy concerns are a major inhibiting factor in their continuance usage of PNS. This study investigates the effect of privacy protection functions provided by PNS sites in enhancing users' perceived fairness on the basis of justice theory to mitigate users' concerns of information privacy. The mechanism underlying such influence on users' continuance usage of PNS comprises privacy concern and privacy-control self-efficacy.

Four scenario-based surveys are conducted to test the proposed hypotheses. The authors test the research model with a sample of 360 participants by ANOVA and PLS.

Results show that the proposed privacy protection functions have direct positive effects on users' privacy-control self-efficacy, negative effects on privacy concern and indirectly affect their continuance usage of PNS. Furthermore, the interaction effects between two approaches have different impacts on users' privacy concern and privacy-control self-efficacy.

This study provides some suggestions and guidance for PNS providers to design effective privacy protection technologies.

The purpose of this research is to examine the effect of information privacy concern on users' social shopping intention.

Based on the 340 valid responses collected from a survey, structural equation modeling (SEM) was employed to examine the research model.

The results indicated that while disposition to privacy positively affects privacy concern, both reputation and laws negatively affect privacy concern, which in turn decreases social shopping intention. In addition, trust partially mediates the effect of privacy concern on social shopping intention.

The results imply that social commerce companies need to mitigate users' privacy concern in order to facilitate their shopping behavior.

This research disclosed that privacy concern receives a tripartite influence from users (disposition to privacy), platforms (reputation) and governments (laws). The results help us gain a complete understanding of information privacy concern mitigation in social shopping.

This study aims to investigate the antecedents of mobile Internet users’ perception of information sensitivity (PIS) and willingness to provide personal information (WTP). It provides insights about how these antecedents influence users’ perceived information sensitivity and willingness to provide.

An online survey of mobile Internet users was conducted in China, generating a total of 1,000 qualified responses for analysis.

Results reveal the differential effects of some major antecedents of mobile Internet users’ perceived information sensitivity and willingness to provide (individual disposition to value privacy, age, gender, app type and privacy concerns) and such impact vary across low-, medium- and high-privacy segments.

This study provides insights into the antecedents of mobile Internet users’ attitudes towards personal information privacy. It also extends the understanding of users’ perceived information sensitivity and willingness to provide such information comparatively among four countries.

The purpose of this research is to disprove the common assumptions of research into privacy concerns from an adversarial paradigm, which does not work in the context of the internet. These assumptions usually claim that internet users who have higher privacy concerns will disclose less information, and that data subjects are always adversarial to data users without considering social contexts.

The study surveyed 400 respondents from China, The Netherlands, Taiwan and the USA. It examined not only their privacy concerns, but also their actual practices, in order to identify any similarities between concerns and practices.

This study proved that internet users' privacy concerns do not reflect their privacy practices and showed how social contexts (Web category) influence users' privacy practices. Respondents from China, The Netherlands, Taiwan and the USA perceive Website categories in different ways, reflecting the influences of political systems, cultural background and economic development.

This study maintains that future research on online privacy should take contexts or situations into account. To confirm this, additional research should be undertaken on how social contexts in other countries affect users' privacy concerns and practices. Investigators should also study what makes users more likely to disclose information.

This study suggests that legislation provides the basic protection, while self‐regulation supplies the detailed principles of online privacy. Privacy education teaches users how to create their “zone of privacy” and how to be responsible for their online practices, in order to build an abuse‐free information environment on the internet.