Interactions between Culture, Regulatory Structure, and Information Privacy across countries

We analyze the relationships between country culture and country regulatory structure pertaining to information privacy concerns (IPC) in the context of social media applications. Drawing on prior research we develop a framework that integrates country culture and country regulatory structure and use it as the basis for a study that contrasts samples of 1086 professionals drawn from four countries – United States, United Kingdoms, India and Hong Kong – to assess effects of national culture and of a nation’s regulatory structure on IPC, attitudinal beliefs about information privacy and professionals’ behavioral reactions to IPC. We find that country culture has a strong bearing on explaining differences in individuals’ IPC concerns, attitudinal beliefs about privacy, and behavioral reactions to privacy much more than does country regulatory structure. Country culture remains a significant factor in the management of information privacy. The results also show that country regulatory structure remains deficient in allaying individuals’ concerns pertaining to information privacy.

Interactions Between Culture, Regulatory Structure, and Information Privacy Across Countries

We analyze the relationships between country culture and country regulatory structure pertaining to information privacy concerns (IPC) in the context of social media applications. Drawing on prior research we develop a framework that integrates country culture and country regulatory structure and use it as the basis for a study that contrasts samples of 1086 professionals drawn from four countries – United States, United Kingdoms, India and Hong Kong – to assess effects of national culture and of a nation’s regulatory structure on IPC, attitudinal beliefs about information privacy and professionals’ behavioral reactions to IPC. We find that country culture has a strong bearing on explaining differences in individuals’ IPC concerns, attitudinal beliefs about privacy, and behavioral reactions to privacy much more than does country regulatory structure. Country culture remains a significant factor in the management of information privacy. The results also show that country regulatory structure remains deficient in allaying individuals’ concerns pertaining to information privacy.

THE QUALITY OF SURVEY SCALES FOR MEASURING INFORMATION PRIVACY CONCERNS ON SOCIAL NETWORK SITES: A SYSTEMATIC REVIEW

Information privacy concerns (IPCs) play an important role in user behavior on social network sites (SNSs). They are associated with self-disclosure behavior, enjoyment, and, perhaps most importantly, a user’s intention and ability to form and sustain social ties on SNSs. While conceptual integration of different approaches to studying IPCs has already been pursued, prior research has pointed to potential problems with respect to the survey measurements of IPCs. More specifically, a plethora of self-assessment scales have been developed but the differences among them have not yet been systematically elaborated, and this is further complicated by many methodologically questionable adaptations of existing IPC survey scales to ever-emerging online contexts and SNS platforms. Accordingly, this study comprises a systematic literature review based on the COSMIN methodology to comprehensively examine the quality of survey scales used for measuring IPCs among SNS users. The results have unveiled significant variety with 35 uni- or multidimensional survey scales used in 71 articles published since 2009. Many of the scales are of questionable quality in terms of structural validity, and only a few of the studies tested them for measurement invariance. Nevertheless, we identified some scales that are promising candidates for future use, although further testing and potential improvements are needed. Our findings could also act as the foundation for a unified measurement approach to IPCs that could be used across different SNSs platforms.

Differential privacy for eye tracking with temporal correlations

New generation head-mounted displays, such as VR and AR glasses, are coming into the market with already integrated eye tracking and are expected to enable novel ways of human-computer interaction in numerous applications. However, since eye movement properties contain biometric information, privacy concerns have to be handled properly. Privacy-preservation techniques such as differential privacy mechanisms have recently been applied to eye movement data obtained from such displays. Standard differential privacy mechanisms; however, are vulnerable due to temporal correlations between the eye movement observations. In this work, we propose a novel transform-coding based differential privacy mechanism to further adapt it to the statistics of eye movement feature data and compare various low-complexity methods. We extend the Fourier perturbation algorithm, which is a differential privacy mechanism, and correct a scaling mistake in its proof. Furthermore, we illustrate significant reductions in sample correlations in addition to query sensitivities, which provide the best utility-privacy trade-off in the eye tracking literature. Our results provide significantly high privacy without any essential loss in classification accuracies while hiding personal identifiers.

Antecedents and Outcomes of Information Privacy Concerns in the Saudi Arabia Social Networking Context

Online information privacy has become a developing social worry that may hinder individuals' social site associations. The motivation behind this investigation is to dissect the effect of an err toward privacy and the social presence of a website on a person's privacy concerns about the website. Data were collected from 650 respondents in Saudi Arabia. The findings indicate a significance influence of informativness, social presence, utility of website, perceived severity, and self-efficacy on website-specific privacy concerns. Furthermore, privacy concerns have a significant influence on behavioural intentions. This study also extends the information privacy literature through the analysis of the drivers and outcomes of online privacy concerns in the social network context.

Future Intention to disclose personal information via mobile apps

This study analyses the future intention to disclose personal information in order to use mobile applications (apps) and the framing effect in relation to privacy concerns. To test the effects, an experiment was conducted involving 405 participants, using a single-factor design with independent groups and covariates. The results indicate concern about privacy is negatively related to the future intention, confirming the effects of framing on future intention, with the effect being negative in relation to the negative framing of trust beliefs and positive in relation to the positive framing of risk beliefs, while the moderating effect was not confirmed. Thus, this paper contributes to two specific areas: 1) privacy, because it confirms the relationship between information privacy concerns and future intention (new proposed scale); and 2) decision-making, as it demonstrates the effects of framing on risk and trust beliefs in future intentions, which, as far as is known, has not previously been shown.

Should We Be Concerned about How Information Privacy Concerns Are Measured in Online Contexts? A Systematic Review of Survey Scale Development Studies

This systematic review addresses problems identified in existing research on survey measurements of individuals’ information privacy concerns in online contexts. The search in this study focused on articles published between 1996 and 2019 and yielded 970 articles. After excluding duplicates and screening for eligibility, we were left with 13 articles in which the investigators developed a total of 16 survey scales. In addition to reviewing the conceptualizations, contexts, and dimensionalities of the scales, we evaluated the quality of methodological procedures used in the scale development process, drawing upon the COnsensus-based Standards for the selection of health Measurement INstruments (COSMIN) Risk of Bias checklist. The results confirmed that the breadth of conceptualizations and dimensions of information privacy concerns are constructed with a low emphasis on contextuality. Assessment of the quality of methodological procedures suggested a need for a more thorough evaluation of content validity. We provide several recommendations for tackling these issues and propose new research directions.

Everybody wants some: Collection and control of personal information, privacy concerns, and social media use

As the utility of social media platforms for interacting with large populations, as well as understanding how they interact, becomes an increasingly interesting area, privacy concerns could present a barrier to engagement. This study employs a survey method to explore social media user frustrations with terms of service agreements and concerns over privacy and personal information shared on them. Findings support that concerns over control, collection, and access to personal information associate with decreased intensity of social media use and correlate to frustration with terms of service agreements regarding personal information use. Given the relationship between use and privacy concerns, leaving privacy concerns unaddressed might also lead to a reduction of use if these concerns continue to grow among users.

Validity and Reliability of the Scale Internet Users’ Information Privacy Concerns (IUIPC)

Internet Users’ Information Privacy Concerns (IUIPC-10) is one of the most endorsed privacy concern scales. It is widely used in the evaluation of human factors of PETs and the investigation of the privacy paradox. Even though its predecessor Concern For Information Privacy (CFIP) has been evaluated independently and the instrument itself seen some scrutiny, we are still missing a dedicated confirmation of IUIPC-10, itself. We aim at closing this gap by systematically analyzing IUIPC’s construct validity and reliability. We obtained three mutually independent samples with a total of N = 1031 participants. We conducted a confirmatory factor analysis (CFA) on our main sample to assert the validity and reliability of IUIPC-10. Having found weaknesses, we proposed a respecified instrument IUIPC-8 with improved psychometric properties. Finally, we confirmed our findings on a validation sample. While we found sound foundations for content validity and could confirm the overall three-dimensionality of IUIPC-10, we observed evidence of biases in the question wording and found that IUIPC-10 consistently missed the mark in evaluations of construct validity and reliability, calling into question the unidimensionality of its sub-scales Awareness and Control. Our respecified scale IUIPC-8 offers a statistically significantly better model and outperforms IUIPC-10’s construct validity and reliability. The disconfirming evidence on IUIPC-10’s construct validity raises doubts how well it measures the latent variable Information Privacy Concern. The less than desired reliability could yield spurious and erratic results as well as attenuate relations with other latent variables, such as behavior. Thereby, the instrument could confound studies of human factors of PETs or the privacy paradox, in general.

PPDM-TAN: A Privacy-Preserving Multi-Party Classifier

Distributed medical, financial, or social databases are analyzed daily for the discovery of patterns and useful information. Privacy concerns have emerged as some database segments contain sensitive data. Data mining techniques are used to parse, process, and manage enormous amounts of data while ensuring the preservation of private information. Cryptography, as shown by previous research, is the most accurate approach to acquiring knowledge while maintaining privacy. In this paper, we present an extension of a privacy-preserving data mining algorithm, thoroughly designed and developed for both horizontally and vertically partitioned databases, which contain either nominal or numeric attribute values. The proposed algorithm exploits the multi-candidate election schema to construct a privacy-preserving tree-augmented naive Bayesian classifier, a more robust variation of the classical naive Bayes classifier. The exploitation of the Paillier cryptosystem and the distinctive homomorphic primitive shows in the security analysis that privacy is ensured and the proposed algorithm provides strong defences against common attacks. Experiments deriving the benefits of real world databases demonstrate the preservation of private data while mining processes occur and the efficient handling of both database partition types.