What I Say Means What I Do: Risk Concerns and Mobile Application-Selection Behaviors.

OBJECTIVE: The goal of this study was to examine the relation between users' reported risk concerns and their choice behaviors in a mobile application (app) selection task. BACKGROUND: Human users are typically regarded as the weakest link in cybersecurity and privacy protection; however, it is possible to leverage the users' predilections to increase security. There have been mixed results on the relation between users' self-reported privacy concerns and their behaviors. METHOD: In three experiments, the timing of self-reported risk concerns was either a few weeks before the app-selection task (pre-screen), immediately before it (pre-task), or immediately after it (post-task). We also varied the availability and placement of clear definitions and quizzes to ensure users' understanding of the risk categories. RESULTS: The post-task report significantly predicted the app-selection behaviors, consistent with prior findings. The pre-screen report was largely inconsistent with the reports implemented around the time of the task, indicating that participants' risk concerns may not be stable over time and across contexts. Moreover, the pre-task report strongly predicted the app-selection behaviors only when elaborated definitions and quizzes were placed before the pre-task question, indicating the importance of clear understanding of the risk categories. CONCLUSION: Self-reported risk concerns may be unstable over time and across contexts. When explained with clear definitions, self-reported risk concerns obtained immediately before or after the app-selection task significantly predicted app-selection behaviors. APPLICATION: We discuss implications for including personalized risk concerns during app selection that enable comparison of alternative mobile apps.

P³DB 3.0: From plant phosphorylation sites to protein networks.

In the past few years, the Plant Protein Phosphorylation Database (P(3)DB, http://p3db.org) has become one of the most significant in vivo data resources for studying plant phosphoproteomics. We have substantially updated P(3)DB with respect to format, new datasets and analytic tools. In the P(3)DB 3.0, there are altogether 47 923 phosphosites in 16 477 phosphoproteins curated across nine plant organisms from 32 studies, which have met our multiple quality standards for acquisition of in vivo phosphorylation site data. Centralized by these phosphorylation data, multiple related data and annotations are provided, including protein-protein interaction (PPI), gene ontology, protein tertiary structures, orthologous sequences, kinase/phosphatase classification and Kinase Client Assay (KiC Assay) data--all of which provides context for the phosphorylation event. In addition, P(3)DB 3.0 incorporates multiple network viewers for the above features, such as PPI network, kinase-substrate network, phosphatase-substrate network, and domain co-occurrence network to help study phosphorylation from a systems point of view. Furthermore, the new P(3)DB reflects a community-based design through which users can share datasets and automate data depository processes for publication purposes. Each of these new features supports the goal of making P(3)DB a comprehensive, systematic and interactive platform for phosphoproteomics research.

Display of major risk categories for android apps.

Communicating cybersecurity risks to mobile-device users is essential. However, existing means of conveying the risks through detailed permission lists are ineffective. Risk indexes that convey overall risk are effective at influencing app-selection decisions, but many users want more information. We examined how users assess the risks associated with downloading applications on the Android platform by comparing various graphical formats of intermediate-level risk displays containing three risk categories: personal privacy; monetary loss; device stability. Bar-graph and table formats were compared, as were vertical and horizontal displays. Participants performed app risk-rating (Experiment 1) and app-selection (Experiments 2 and 3) tasks for hypothetical apps with risk scores on each of the categories. They also specified which risk category was of most concern to them. Increased risk scores in each category led to higher rated risk and lower app-selection rate which matched with self-reported risk concerns. Bar-graphs were more time-efficient and yielded higher risk-ratings than tables, although the two formats did not differ in the app-selection task. Moreover, horizontal bar-graphs yielded faster responses than vertical bar-graphs. The results indicate that the intermediate-level risk display was effective in conveying risk-category information, especially with horizontal bar-graphs, and personalized design of this display based on users' risk concerns is potentially useful. (PsycINFO Database Record