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This paper aims to contribute to the ongoing discourse about the nature of privacy and its role in ubiquitous environments and provide insights for future research.

The paper analyses the privacy implications of particular characteristics of ubiquitous applications and discusses the fundamental principles and information practices used in digital environments for protecting individuals' private data.

A significant trend towards shifting privacy protection responsibility from government to the individuals is identified. Also, specific directions for future research are provided with a focus on interdisciplinary research.

This paper identifies key research issues and provides directions for future research.

This study contributes by identifying major challenges that should be addressed, so that a set of “fair information principles” can be applied in the context of ubiquitous environments. It also discusses the limitations of these principles and provides recommendations for future research.

Human genomic research (HGR) demands very large pools of data to generate meaningful inference. Yet, the sharing of one’s genetic data for research is a voluntary act. The collection of data sufficient to fuel rapid advancement is contingent on individuals’ willingness to share. Privacy risks associated with sharing this unique and intensely personal data are significant. Genetic data are an unambiguous identifier. Public linkage of donor to their genetic data could reveal predisposition to diseases, behaviors, paternity, heredity, intelligence, etc. The purpose of this paper is to understand individuals’ willingness to volunteer their private information in this high-risk/high-reward context.

The authors collect survey data from 273 respondents and use structural equation modeling techniques to analyze responses.

The authors find statistical support for our theorization. They find that while heightened awareness of the benefits and risks of sharing correlates with increased privacy concerns, the net impact is an increase in intention to share.

The findings suggest that prescriptive awareness might be an effective tool with which policy-makers can gain the sufficient voluntary participation from individuals necessary to drive large-scale medical research.

This study contributes a theoretically and empirically informed model which demonstrates the impact of awareness and privacy concern on individuals’ willingness to share their genetic data for large-scale HGR. It helps inform a rising class of data sufficiency problems related to large-scale medical research.

The purpose of this paper is to solve the problem of information privacy and security of social users. Mobile internet and social network are more and more deeply integrated into people’s daily life, especially under the interaction of the fierce development momentum of the Internet of Things and diversified personalized services, more and more private information of social users is exposed to the network environment actively or unintentionally. In addition, a large amount of social network data not only brings more benefits to network application providers, but also provides motivation for malicious attackers. Therefore, under the social network environment, the research on the privacy protection of user information has great theoretical and practical significance.

In this study, based on the social network analysis, combined with the attribute reduction idea of rough set theory, the generalized reduction concept based on multi-level rough set from the perspectives of positive region, information entropy and knowledge granularity of rough set theory were proposed. Furthermore, it was traversed on the basis of the hierarchical compatible granularity space of the original information system and the corresponding attribute values are coarsened. The selected test data sets were tested, and the experimental results were analyzed.

The results showed that the algorithm can guarantee the anonymity requirement of data publishing and improve the effect of classification modeling on anonymous data in social network environment.

In the test and verification of privacy protection algorithm and privacy protection scheme, the efficiency of algorithm and scheme needs to be tested on a larger data scale. However, the data in this study are not enough. In the following research, more data will be used for testing and verification.

In the context of social network, the hierarchical structure of data is introduced into rough set theory as domain knowledge by referring to human granulation cognitive mechanism, and rough set modeling for complex hierarchical data is studied for hierarchical data of decision table. The theoretical research results are applied to hierarchical decision rule mining and k-anonymous privacy protection data mining research, which enriches the connotation of rough set theory and has important theoretical and practical significance for further promoting the application of this theory. In addition, combined the theory of secure multi-party computing and the theory of attribute reduction in rough set, a privacy protection feature selection algorithm for multi-source decision table is proposed, which solves the privacy protection problem of feature selection in distributed environment. It provides a set of effective rough set feature selection method for privacy protection classification mining in distributed environment, which has practical application value for promoting the development of privacy protection data mining.

In this study, the proposed algorithm and scheme can effectively protect the privacy of social network data, ensure the availability of social network graph structure and realize the need of both protection and sharing of user attributes and relational data.

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.

As social network sites (SNS) have increasingly become one of the most important channels for communication, the related privacy issues gain more and more attention in both industry and academic research fields. This study aims to connect the antecedents of information privacy disclosure on SNS.

Based on exchange theory, this study tries to investigate the decision-making process for information privacy disclosure on SNS. Factors from both user’s and website’s perspectives are taken into account in the proposed model.

The results suggest that an individual’s perceived benefits will increase their willingness to disclose information privacy on SNS, but perceived risks decrease this kind of willingness. The authors also find social network size, personal innovativeness and incentive provision positively affect people’s perceived benefits.

Moreover, privacy invasion experience enhances perceived personal risks, but website reputation helps to reduce perceived risks.

In today’s digitized environment, information privacy has become a prime concern for everybody. The purpose of this paper is to provide an understanding of information privacy concern arising because of the application of computer-based information system in the various domains (E-Governance, E-Commerce, E-Health, E-Banking and E-Finance), and at different levels, i.e. individual, group, organizational and societal.

The authors performed an in-depth analysis of different research articles related to information privacy concerns and elements affecting those at certain level of applications. The primary sources of literature were articles retrieved from online databases. Various online journal and scholarly articles were searched in detail to locate information privacy-related articles.

The authors have carried out a detailed literature review to identify the different levels where the privacy is a big challenging task. This paper provides insights whether information privacy concern may obstruct in the successful dispersal and adoption of different applications in various application domains. Consumers’ attitude towards information privacy concerns have enlightened and addressed at individual levels in numerous domains. Privacy concerns at the individual level, as suggested by our analysis, seem to have been sufficiently addressed or addressed. However, information privacy concerns at other levels – group, organizational and societal levels – need the attention of researchers.

In this paper, the authors have posited that it will help the researchers to more focus at group level privacy perspective in the information privacy era.

Based on communication privacy management (CPM) theory, this study aimed to explore how gender and age criteria, motivational criterion (locatability), benefit-risk ratio criteria (perceived benefits of privacy information disclosure and privacy concerns), contextual criteria (privacy experience) and boundary coordination and turbulence (perceived effectiveness of privacy policy) interact and combine in leading to voice assistant (VA) users' intention to reveal or conceal their privacy information.

A questionnaire was designed and distributed to car drivers who have intelligent VA use experience while driving. A total of 230 valid responses were collected. The fuzzy-set qualitative comparative analysis (fsQCA) approach was used to analyse the data.

Via configuration analysis with fsQCA, five equivalent configurations leading to the high intention of disclosing privacy information, and one configuration leading to the intention of concealing privacy information were identified. Four theoretical configurational propositions were proposed. Moreover, the theoretical and practical implications of the findings are discussed.

This study offers some insightful implications in the field of private information communication in artificial intelligence applications as well as practical implications for intelligent VA application design. The findings provide empirical evidence of privacy communication management in the context of smart VA usage.

Previous studies have attempted to address online uncertainties from the relationship marketing perspective. The purpose of this paper is to argue that the integration of media richness theory (MRT) and cognitive fit theory (CFT) can contribute a new perspective in addressing consumers’ transaction-specific uncertainties in online retailing.

On the basis of MRT and CFT, a research model was developed by correlating online channel media richness (OCMR), online–offline information integration (OOII), information privacy concern, perceived deception and online loyalty. The model was empirically examined based on survey data collected from 258 multi-channel consumers in China.

An analysis of structural equation model showed that OCMR is negatively associated with information privacy concern and perceived deception but is not significant to online loyalty. Information privacy concern has a negative influence on online loyalty, but the effect of perceived deception is not significant. Moreover, information privacy concern is positively related to perceived deception. The OOII strengthens the influence of OCMR but not the moderating effect of integrated promotion, product and price information on the relationship between OCMR and online loyalty.

This study contributes to the extant literature on online retailing by examining the effect of OCMR on online transaction uncertainties. Information integrity in the form of OOII was proposed to complement OCMR. Results have shown that OCMR is significant in reducing online uncertainties, and OOII strengthens this effect, thereby enhancing online loyalty.

This investigation aims to examine the differences and similarities between activity tracking technology users from two regions (the USA and Germany) in their intended privacy-related behavior. The focus lies on data handling after hypothetical discontinuance of use, data protection and privacy policy seeking, and privacy concerns.

The data was collected through an online survey in 2019. In order to identify significant differences between participants from Germany and the USA, the chi-squared test and the Mann–Whitney U test were applied.

The intensity of several privacy-related concerns was significantly different between the two groups. The majority of the participants did not inform themselves about the respective data privacy policies or terms and conditions before installing an activity tracking application. The majority of the German participants knew that they could request the deletion of all their collected data. In contrast, only 35% out of 68 participants from the US knew about this option.

This study intends to raise awareness about managing the collected health and fitness data after stopping to use activity tracking technologies. Furthermore, to reduce privacy and security concerns, the involvement of the government, companies and users is necessary to handle and share data more considerably and in a sustainable way.

This study sheds light on users of activity tracking technologies from a broad perspective (here, participants from the USA and Germany). It incorporates not only concerns and the privacy paradox but (intended) user behavior, including seeking information on data protection and privacy policy and handling data after hypothetical discontinuance of use of the technology.

The purpose of this paper is to draw on the elaboration likelihood model to examine location-based services (LBS) users’ privacy concern.

Based on the 266 valid responses collected from a survey, structural equation modeling was employed to examine the research model.

The results indicated that privacy concern receives a dual influence from both central cues and peripheral cues. Central cues include privacy policy and information quality, whereas peripheral cues include reputation and privacy seals. Privacy control moderates the effects of privacy policy and privacy seals on privacy concern.

The results imply that service providers need to consider both central and peripheral cues in order to mitigate users’ privacy concern associated with using LBS.

Although previous research has found the effect of privacy concern on user adoption of LBS, it has seldom examined the influence processes of external factors on privacy concern. This research tries to fill the gap.