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1. Predicting smartphone location-sharing decisions through self-reflection on past privacy behavior

   https://doi.org/10.1093/cybsec/tyaa014  

   Wisniewski, Pamela ; Safi, Muhammad Irtaza ; Patil, Sameer ; Page, Xinru ( January 2020 , Journal of Cybersecurity)

   null (Ed.)

   Abstract Smartphone location sharing is a particularly sensitive type of information disclosure that has implications for users’ digital privacy and security as well as their physical safety. To understand and predict location disclosure behavior, we developed an Android app that scraped metadata from users’ phones, asked them to grant the location-sharing permission to the app, and administered a survey. We compared the effectiveness of using self-report measures commonly used in the social sciences, behavioral data collected from users’ mobile phones, and a new type of measure that we developed, representing a hybrid of self-report and behavioral data to contextualize users’ attitudes toward their past location-sharing behaviors. This new type of measure is based on a reflective learning paradigm where individuals reflect on past behavior to inform future behavior. Based on data from 380 Android smartphone users, we found that the best predictors of whether participants granted the location-sharing permission to our app were: behavioral intention to share information with apps, the “FYI” communication style, and one of our new hybrid measures asking users whether they were comfortable sharing location with apps currently installed on their smartphones. Our novel, hybrid construct of self-reflection on past behavior significantly improves predictive power and shows the importance of combining social science and computational science approaches for improving the prediction of users’ privacy behaviors. Further, when assessing the construct validity of the Behavioral Intention construct drawn from previous location-sharing research, our data showed a clear distinction between two different types of Behavioral Intention: self-reported intention to use mobile apps versus the intention to share information with these apps. This finding suggests that users desire the ability to use mobile apps without being required to share sensitive information, such as their location. These results have important implications for cybersecurity research and system design to meet users’ location-sharing privacy needs. 

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2. Using Smartphone Sensor Data to Assess Inhibitory Control in the Wild: Longitudinal Study

   https://doi.org/10.2196/21703  

   Tseng, Vincent WS ; Costa, Jean Dos ; Jung, Malte F ; Choudhury, Tanzeem ( January 2020 , JMIR mHealth and uHealth)

   Background Inhibitory control, or inhibition, is one of the core executive functions of humans. It contributes to our attention, performance, and physical and mental well-being. Our inhibitory control is modulated by various factors and therefore fluctuates over time. Being able to continuously and unobtrusively assess our inhibitory control and understand the mediating factors may allow us to design intelligent systems that help manage our inhibitory control and ultimately our well-being. Objective The aim of this study is to investigate whether we can assess individuals’ inhibitory control using an unobtrusive and scalable approach to identify digital markers that are predictive of changes in inhibitory control. Methods We developed InhibiSense, an app that passively collects the following information: users’ behaviors based on their phone use and sensor data, the ground truths of their inhibition control measured with stop-signal tasks (SSTs) and ecological momentary assessments (EMAs), and heart rate information transmitted from a wearable heart rate monitor (Polar H10). We conducted a 4-week in-the-wild study, where participants were asked to install InhibiSense on their phone and wear a Polar H10. We used generalized estimating equation (GEE) and gradient boosting tree models fitted with features extracted from participants’ phone use and sensor data to predict their stop-signal reaction time (SSRT), an objective metric used to measure an individual’s inhibitory control, and identify the predictive digital markers. Results A total of 12 participants completed the study, and 2189 EMAs and SST responses were collected. The results from the GEE models suggest that the top digital markers positively associated with an individual’s SSRT include phone use burstiness (P=.005), the mean duration between 2 consecutive phone use sessions (P=.02), the change rate of battery level when the phone was not charged (P=.04), and the frequency of incoming calls (P=.03). The top digital markers negatively associated with SSRT include the standard deviation of acceleration (P<.001), the frequency of short phone use sessions (P<.001), the mean duration of incoming calls (P<.001), the mean decibel level of ambient noise (P=.007), and the percentage of time in which the phone was connected to the internet through a mobile network (P=.001). No significant correlation between the participants’ objective and subjective measurement of inhibitory control was found. Conclusions We identified phone-based digital markers that were predictive of changes in inhibitory control and how they were positively or negatively associated with a person’s inhibitory control. The results of this study corroborate the findings of previous studies, which suggest that inhibitory control can be assessed continuously and unobtrusively in the wild. We discussed some potential applications of the system and how technological interventions can be designed to help manage inhibitory control. 

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3. The Movebank system for studying global animal movement and demography

   https://doi.org/10.1111/2041-210X.13767  

   Kays, Roland ; Davidson, Sarah C. ; Berger, Matthias ; Bohrer, Gil ; Fiedler, Wolfgang ; Flack, Andrea ; Hirt, Julian ; Hahn, Clemens ; Gauggel, Dominik ; Russell, Benedict ; et al ( December 2021 , Methods in Ecology and Evolution)

   Quantifying movement and demographic events of free‐ranging animals is fundamental to studying their ecology, evolution and conservation. Technological advances have led to an explosion in sensor‐based methods for remotely observing these phenomena. This transition to big data creates new challenges for data management, analysis and collaboration.

   We present the Movebank ecosystem of tools used by thousands of researchers to collect, manage, share, visualize, analyse and archive their animal tracking and other animal‐borne sensor data. Users add sensor data through file uploads or live data streams and further organize and complete quality control within the Movebank system. All data are harmonized to a data model and vocabulary. The public can discover, view and download data for which they have been given access to through the website, the Animal Tracker mobile app or by API. Advanced analysis tools are available through the EnvDATA System, the MoveApps platform and a variety of user‐developed applications. Data owners can share studies with select users or the public, with options for embargos, licenses and formal archiving in a data repository.

   Movebank is used by over 3,100 data owners globally, who manage over 6 billion animal location and sensor measurements across more than 6,500 studies, with thousands of active tags sending over 3 million new data records daily. These data underlie >700 published papers and reports. We present a case study demonstrating the use of Movebank to assess life‐history events and demography, and engage with citizen scientists to identify mortalities and causes of death for a migratory bird.

   A growing number of researchers, government agencies and conservation organizations use Movebank to manage research and conservation projects and to meet legislative requirements. The combination of historic and new data with collaboration tools enables broad comparative analyses and data acquisition and mapping efforts. Movebank offers an integrated system for real‐time monitoring of animals at a global scale and represents a digital museum of animal movement and behaviour. Resources and coordination across countries and organizations are needed to ensure that these data, including those that cannot be made public, remain accessible to future generations.

    

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4. Creating Public Value by Democratizing the Ecosystem of Human Service Providers

   https://doi.org/10.1145/3308560.3316487  

   Chelmis, Charalampos ; Yao, Mengfan ( January 2019 , Companion Proceedings of The 2019 World Wide Web Conference)

   Human service providers play a critical role in improving well–being in the United States. However, little is know about (i) how service seekers find the services they are looking for by navigating among available service providers, and (ii) how such organizations collaborate to meet human needs. In this paper, we report the first outcomes of our ongoing project. Specifically, we first describe a data acquisition engine, designed around the particular challenges of capturing, maintaining, and updating data pertaining to human service organizations from semistructured Web sources. We then proceed to illustrate the potential of the resulting comprehensive repository of human service providers through a case study showcasing a mobile app prototype designed to provide a one–stop shop for human service seekers. 

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5. Protecting Million-User iOS Apps with Obfuscation: Motivations, Pitfalls, and Experience

   Wang, Pei ; Wu, Dinghao ; Chen, Zhaofeng ; Wei, Tao. ( May 2018 , Proceedings of the 40th International Conference on Software Engineering (ICSE 2018), Software Engineering in Practice (SEIP) Track)

   In recent years, mobile apps have become the infrastructure of many popular Internet services. It is now fairly common that a mobile app serves a large number of users across the globe. Different from web- based services whose important program logic is mostly placed on remote servers, many mobile apps require complicated client-side code to perform tasks that are critical to the businesses. The code of mobile apps can be easily accessed by any party after the software is installed on a rooted or jailbroken device. By examining the code, skilled reverse engineers can learn various knowledge about the design and implementation of an app. Real-world cases have shown that the disclosed critical information allows malicious parties to abuse or exploit the app-provided services for unrightful profits, leading to significant financial losses for app vendors. One of the most viable mitigations against malicious reverse engineering is to obfuscate the software before release. Despite that security by obscurity is typically considered to be an unsound protection methodology, software obfuscation can indeed increase the cost of reverse engineering, thus delivering practical merits for protecting mobile apps. In this paper, we share our experience of applying obfuscation to multiple commercial iOS apps, each of which has millions of users. We discuss the necessity of adopting obfuscation for protecting modern mobile business, the challenges of software obfuscation on the iOS platform, and our efforts in overcoming these obstacles. Our report can benefit many stakeholders in the iOS ecosystem, including developers, security service providers, and Apple as the administrator of the ecosystem. 

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