This study investigates how website design features, web page order and visual complexity, influence users’ initial website aesthetic impressions and how such impressions subsequently enhance engagement and intention to use the website. A laboratory experiment was conducted to test the hypotheses using different levels of web page order (high vs. low), visual complexity (high vs. low), and exposure time (one-second vs. no-time-constraint). Overall, the results from structural equation modeling (SEM) analysis suggest that web page order significantly influences visual appeal, engagement, and intention. In addition, the results of multigroup SEM analysis reveal that users evaluate website design very quickly (within 1 s), and that these evaluations remain remarkably consistent over time.

Commercial organizations and government agencies that gather, store, share and disseminate data are facing increasing concerns over individual privacy and confidentiality. Confidential data is often masked in the database or prior to release to a third party, through methods such as data perturbation. In this study, re-identification risks of three major additive data perturbation techniques were compared using two different record linkage techniques. The results suggest that re-identification risk of Kim's multivariate noise addition method is similar to that of simple noise addition method. The general additive perturbation method (GADP) has the lowest re-identification risk and therefore provides the highest level of protection. The study also suggests that Fuller's method of assessing re-identification risk may be better suited than the probabilistic record-linkage method of Winkler, for numeric data. The results of this study should be help organizations and government agencies choose an appropriate additive perturbation technique.

Government agencies and other organizations commonly release or share microdata for purposes of analysis. In many cases, microdata release needs to preserve the privacy of individuals and/or sensitive attributes. Current measures of privacy of released microdata are often based on empirical assessments of identity and value disclosure. The disadvantage of empirical assessments of privacy is that their results cannot be generalized with confidence across datasets or protection methods. While theoretical definitions of privacy are available for other methods of data release such as query-response output perturbation systems, they are unsuitable for the microdata release context. This study proposes a theoretical basis for measuring privacy in the microdata release context based on statistical dependence. Using this theoretical basis, we develop practical privacy measures that possess several desirable properties, including generalizability. We illustrate the conceptual benefits of this approach and also show that a privacy measure based on statistical dependence can be used effectively for assessing privacy in microdata

Statistical databases often use random data perturbation (RDP) methods to protect against disclosure of confidential numerical attributes. One of the key requirements of RDP methods is that they provide the appropriate level of security against snoopers who attempt to obtain information on confidential attributes through statistical inference. In this study, we evaluate the security provided by three methods of perturbation. The results of this study allow the database administrator to select the most effective RDP method that assures adequate protection against disclosure of confidential information.