In this paper, we examine the psychological antecedents of privacy management strategies in social network sites (SNS) and extend the understanding to collective privacy management. By surveying Facebook users in the US (N = 454), Singapore (N = 467) and South Korea (N = 472), we are able to test our prediction model in these three countries to examine whether the effects of the antecedents are robust from a cross-country perspective. Although some of the effects are significantly different between the three countries, we find that in general users' privacy attitudes, social norms, and self/collective control beliefs substantially predict the adoption of collective privacy management strategies. These findings contribute to the explanations of users' adoption of behavioral privacy management strategies. We conclude with global and country-specific recommendations regarding future privacy designs for collective privacy management.

Background Despite declining cancer incidence and mortality rates, Latina patients continue to have lower 5-year survival rates compared to their non-Hispanic white counterparts. Much of this difference has been attributed to lack of healthcare access and poorer quality of care. Research, however, has not considered the unique healthcare experiences of Latina patients. Methods Latina women with prior diagnoses of stage 0-III breast cancer were asked to complete a cross-sectional survey assessing several socio-demographic factors along with their experiences as cancer patients. Using a series of linear regression models in a sample of 68 Mexican-American breast cancer survivors, we examined the extent to which patients' ratings of provider interpersonal quality of care were associated with patients' overall healthcare quality, and how these associations varied by acculturation status. Results Findings for Latina women indicated that both participatory decision-making (PDM) (β = 0.62, p < .0001) and trust (β = 0.53, p = .02) were significantly associated with patients' ratings of healthcare quality. The interaction between acculturation and PDM further suggested that participating in the decision-making process mattered more for less acculturated than for more acculturated patients (β = -0.51, p ≤ .01). Conclusions The variation across low and high acculturated Latinas in their decision-making process introduces a unique challenge to health care providers. Further understanding the relationship between provider-patient experiences and ratings of overall healthcare quality is critical for ultimately improving health outcomes.

CAPTCHAs have been widely used as an anti-bot means for well over a decade. Unfortunately, they are often hard and annoying to use, and human errors have been blamed mainly on overly complex challenges, or poor challenge design. However, errors can also occur because of ambient sensory distractions, and performance impact of these distractions has not been thoroughly examined. The goal of our work is to explore the impact of auditory distractions on CAPTCHA performance. To this end, we conducted a comprehensive user study. Its results, discussed in this paper, show that various types of auditory stimuli impact performance differently. Generally, simple and less dynamic stimuli sometimes improve subject performance, while highly dynamic stimuli have a negative impact. This is troublesome since CAPTCHAs are often used to protect web sites offering tickets for limited-quantity events, that sell out very quickly, i.e., within seconds. In such settings, introduction of even a small delay can make the difference between obtaining tickets from the primary source, and being forced to use a secondary market. Our study was conducted in a fully automated experimental environment to foster uniform and scalable experiments. We discuss both benefits and limitations of unattended automated experiment paradigm.

Friend request acceptance and information disclosure constitute 2 important privacy decisions for users to control the flow of their personal information in social network sites (SNSs). These decisions are greatly influenced by contextual characteristics of the request. However, the contextual influence may not be uniform among users with different levels of privacy concerns. In this study, we hypothesize that users with higher privacy concerns may consider contextual factors differently from those with lower privacy concerns. By conducting a scenario‐based survey study and structural equation modeling, we verify the interaction effects between privacy concerns and contextual factors. We additionally find that users' perceived risk towards the requester mediates the effect of context and privacy concerns. These results extend our understanding about the cognitive process behind privacy decision making in SNSs. The interaction effects suggest strategies for SNS providers to predict user's friend request acceptance and to customize context‐aware privacy decision support based on users' different privacy attitudes.

If one wants to study privacy from an intercultural perspective, one must first validate whether there are any cultural variations in the concept of “privacy” itself. This study systematically examines cultural differences in collective privacy management strategies, and highlights methodological precautions that must be taken in quantitative intercultural privacy research. Using survey data of 498 Facebook users from the US, Singapore, and South Korea, we test the validity and cultural invariance of the measurement model and predictive model associated with collective privacy management. The results show that the measurement model is only partially culturally invariant, indicating that social media users in different countries interpret the same instruments in different ways. Also, cross-national comparisons of the structural model show that causal pathways from collective privacy management strategies to privacy-related outcomes vary significantly across countries. The findings suggest significant cultural variations in privacy management practices, both with regard to the conceptualization of its theoretical constructs, and with respect to causal pathways.

Human errors in performing security-critical tasks are typically blamed on the complexity of those tasks. However, such errors can also occur because of (possibly unexpected) sensory distractions. A sensory distraction that produces negative effects can be abused by the adversary that controls the environment. Meanwhile, a distraction with positive effects can be artificially introduced to improve user performance. The goal of this work is to explore the effects of visual stimuli on the performance of security-critical tasks. To this end, we experimented with a large number of subjects who were exposed to a range of unexpected visual stimuli while attempting to perform Bluetooth Pairing. Our results clearly demonstrate substantially increased task completion times and markedly lower task success rates. These negative effects are noteworthy, especially, when contrasted with prior results on audio distractions which had positive effects on performance of similar tasks. Experiments were conducted in a novel (fully automated and completely unattended) experimental environment. This yielded more uniform experiments, better scalability and significantly lower financial and logistical burdens. We discuss this experience, including benefits and limitations of the unattended automated experiment paradigm.

With the advent of the Internet of Things (IoT), users are more likely to have privacy concerns since their personal information could be collected, analyzed, and utilized without notice by the networked IoT devices and services. Users may want to control all such activities by explicitly expressing their privacy preferences. However, it is becoming increasingly difficult for users to do so, not only because of the cognitive burden of continuously making privacy decisions for IoT services, but also because IoT devices have no, or only very restricted, user interfaces. Intelligent software helping users make better privacy decisions will be an important component of privacy-preserving IoT environments. In order to construct such a component, we aim to verify whether it will be possible to computationally model and predict users' privacy preferences in IoT. To that end, we survey 172 participants in a simulated campuswide IoT environment about their privacy preferences regarding hypothetical personal information tracking scenarios. Then, we cluster the scenarios based on the survey responses, arriving at four clusters with distinct associated privacy preferences. Based on the clustering results, we uncover contextual factors that induce privacy violations in IoT. Finally, we build machine learning models to predict users' privacy decisions, using both contextual information and the corresponding cluster membership as training data. The final trained model shows 77% accuracy in predicting users' decisions whether or not to allow the respective IoT scenario.

Smart doorbells allow home owners to receive alerts when a visitor is at the door, see who the guest is, and communicate with the visitor from a smart device. They greatly improve people's life quality and contribute to the evolution of smart homes. However, the commercial smart doorbells are quite expensive, usually cost more than 190 US dollars, which is a substantial impediment on the pervasiveness of smart doorbells. To solve this problem, we introduce the Dashbell-a budget smart doorbell system for home use. It connects a WiFi-enabled device, the Amazon Dash Button, to a network and enables the home owner to answer the bell triggered by the dash button using a smartphone. The Dashbell system also enables fast fault detection and diagnosis due to its distributed framework.

The importance of “context” in people’s privacy decisions is widely recognized, mostly in the area of inter-personal privacy. A comprehensive multinational analysis of what users consider to be the main contextual factors impacting their privacy decisions is still largely missing though, rendering it difficult to integrate context into data processing systems and privacy policy frameworks. We present a qualitative study in 4 countries followed by a largescale (N=9,625) quantitative study in 8 countries aimed at identifying the contextual, attitudinal and demographic determinants that influence individuals' acceptance of scenarios involving the use of their personal data, and at gauging the relative influence of these determinants globally and country-wise. We develop parsimonious regression models to analyze the relative importance of different factors in different countries. The implications of such models in developing context and privacy aware systems and privacy policy frameworks are discussed.

Widespread adoption of smartphones brought significant technical advances in technology in today's world. As a consequence, however, it has almost become impossible for us to separate our personal lives from work. Work-life balance is hard to achieve in the current scenario: our business emails keep arriving on our phones even when we are at home or on the beach. Our research goal is to develop and deploy a location-aware messaging framework that is based on indoor location detection. This framework will only deliver messages when users are at work. The system uses beacon technologies to accurately determine the position of a user inside a building. The users, in return, communicate with the system using an application running on their smartphones. The framework delivers customized messages according to the user (i.e. it can distinguish between the owner and visitor in the same location).

Many computer users today value personalization but perceive it in conflict with their desire for privacy. They therefore tend not to disclose data that would be useful for personalization. We investigate how characteristics of the personalization provider influence users' attitudes towards personalization and their resulting disclosure behavior. We propose an integrative model that links these characteristics via privacy attitudes to actual disclosure behavior. Using the Elaboration Likelihood Model, we discuss in what way the influence of the manipulated provider characteristics is different for users engaging in different levels of elaboration (represented by the user characteristics of privacy concerns and self‐efficacy). We find particularly that (a) reputation management is effective when users predominantly use the peripheral route (i.e., a low level of elaboration), but much less so when they predominantly use the central route (i.e., a high level of elaboration); (b) client‐side personalization has a positive impact when users use either route; and (c) personalization in the cloud does not work well in either route. Managers and designers can use our results to instill more favorable privacy attitudes and increase disclosure, using different techniques that depend on each user's levels of privacy concerns and privacy self‐efficacy.

Self-tracking of food intake has been studied at length, but many challenges still remain. Current systems often require significant effort from users, and work that has tried to reduce it resulted in low accuracy or delays. Effort is a major barrier to long term use of self-tracking systems. We propose CalNag, a system that integrates a weighing scale, a barcode reader, and a cloud based service. Together, they allow users to track accurate calorie consumption when preparing food at home, requiring minimal effort for each interaction with the system. Through hand-geometry bio-identification, CalNag seamlessly works for multiple users. We have developed a working prototype and conducted a pilot user study. Our results suggest that CalNag's architecture is a promising solution to effectively promote long term self-tracking of diet.