Managing Software Engineering Student Teams Using Pellerin\u27s 4-D System

In this article, we discuss the use of Pellerin’s Four Dimension Leadership System (4-D) as a way to manage teams in a classroom setting. Over a 5-year period, we used a modified version of the 4-D model to manage teams within a senior level Software Engineering capstone course. We found that this approach for team management in a classroom setting led to qualitatively fewer incidents of teams unable to effectively work together, better projects, and greater group cohesion. In this article, we discuss our experience using the 4-D System, which was not originally designed for use in the classroom. We find our modified version of the 4-D System to be viable in a classroom setting and provide the reader with everything needed to implement 4-D in his or her own course

CONFIGURATION OF APPLICATION PERMISSIONS WITH CONTEXTUAL ACCESS CONTROL

Users are burdened with the task of configuring access control policies on many dif- ferent application platforms used by mobile devices and social network sites. Many of these platforms employ access control mechanisms to configure application per- missions before the application is first used and provide an all or nothing decision for the user. When application platforms provide fine grained control over decision making, many users exhibit behavior that indicates they desire more control over their application permissions. However, users who desire control over application permissions still struggle to properly configure them because they lack the context in which to make better decisions. In this dissertation, I attempt to address these problems by exploring decision making during the context of using mobile and social network applications. I hypothesize that users are able to better configure access control permissions as they interact with applications by supplying more contextual information than is available when the application is being installed. I also explore how logged access data generated by the application platform can provide users with more understanding of when their data is accessed. Finally, I examine the effects that this contextually improved application platform has on user decision making

+Your Circles: Sharing Behavior on Google+

Users are sharing and consuming enormous amounts of information through online social network interaction every day. Yet, many users struggle to control what they share to their overlapping social spheres. Google+ introduces circles, a mechanism that enables users to group friends and use these groups to control their social network feeds and posts. We present the results of a qualitative interview study on the sharing perceptions and behavior of 27 Google+ users. These results indicate that many users have a clear understanding of circles, using them to target information to those most interested in it. Yet, despite these positive perceptions, there is only moderate use of circles to control information flow. We explore reasons and risks associated with these behaviors and provide insight on the impact and open questions of this privacy mechanism

The impact of social navigation on privacy policy configuration

Social navigation is a promising approach to help users make better privacy and security decisions using community knowledge and expertise. Social navigation has recently been applied to several privacy and security systems such as peer-topeer file sharing, cookie management, and firewalls. However, little empirical evaluation of social navigation cues has been performed in security or privacy systems to understand the real impact such knowledge has on user behavior and the resulting policies. In this paper, we explore the application of social navigation to access control policy configuration using an empirical between subjects study. Our results indicate that community information does impact user behavior, but only when the visual representation of the cue is sufficiently strong

Mapping user preference to privacy default settings

Copyright © 2015 ACM. Managing the privacy of online information can be a complex task often involving the configuration of a variety of settings. For example, Facebook users determine which audiences have access to their profile information and posts, how friends can interact with them through tagging, and how others can search for them-and many more privacy tasks. In most cases, the default privacy settings are permissive and appear to be designed to promote information sharing rather than privacy. Managing privacy online can be complex and often users do not change defaults or use granular privacy settings. In this article, we investigate whether default privacy settings on social network sites could be more customized to the preferences of users. We survey users\u27 privacy attitudes and sharing preferences for common SNS profile items. From these data, we explore using audience characterizations of profile items to quantify fit scores that indicate how well default privacy settings represent user privacy preferences. We then explore the fit of various schemes, including examining whether privacy attitude segmentation can be used to improve default settings. Our results suggest that using audience characterizations from community data to create default privacy settings can better match users\u27 desired privacy settings

Investigating user perceptions of mobile app privacy: An analysis of user-submitted app reviews

© 2020, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited. Mobile devices and third-party applications are used by over 4.5 billion people worldwide. Third-party applications often request or even require authorized access to personal information through mobile device components. Application developers explain the need for access in their privacy policies, yet many users are concerned about the privacy implications of allowing access to their personal information. This article explores how user perceptions of privacy affect user sentiment by analyzing over five million user-submitted text reviews and star ratings collected over a four-year period. The authors use supervised machine learning to classify privacy and non-privacy-related reviews. The authors then use natural language processing sentiment analysis to compare differences between the groups. Additionally, the article explores various aspects of both privacy and non-privacy-related reviews using self-reported measurements such as star rating and helpfulness tags

Investigating user perceptions of mobile app privacy: An analysis of user-submitted app reviews

© 2020, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited. Mobile devices and third-party applications are used by over 4.5 billion people worldwide. Third-party applications often request or even require authorized access to personal information through mobile device components. Application developers explain the need for access in their privacy policies, yet many users are concerned about the privacy implications of allowing access to their personal information. This article explores how user perceptions of privacy affect user sentiment by analyzing over five million user-submitted text reviews and star ratings collected over a four-year period. The authors use supervised machine learning to classify privacy and non-privacy-related reviews. The authors then use natural language processing sentiment analysis to compare differences between the groups. Additionally, the article explores various aspects of both privacy and non-privacy-related reviews using self-reported measurements such as star rating and helpfulness tags