On the Unicity of Smartphone Applications

Prior works have shown that the list of apps installed by a user reveal a lot about user interests and behavior. These works rely on the semantics of the installed apps and show that various user traits could be learnt automatically using off-the-shelf machine-learning techniques. In this work, we focus on the re-identifiability issue and thoroughly study the unicity of smartphone apps on a dataset containing 54,893 Android users collected over a period of 7 months. Our study finds that any 4 apps installed by a user are enough (more than 95% times) for the re-identification of the user in our dataset. As the complete list of installed apps is unique for 99% of the users in our dataset, it can be easily used to track/profile the users by a service such as Twitter that has access to the whole list of installed apps of users. As our analyzed dataset is small as compared to the total population of Android users, we also study how unicity would vary with larger datasets. This work emphasizes the need of better privacy guards against collection, use and release of the list of installed apps.Comment: 10 pages, 9 Figures, Appeared at ACM CCS Workshop on Privacy in Electronic Society (WPES) 201

Reality-Mining with Smartphones: Detecting and Predicting Life Events based on App Installation Behavior

Life events are often described as major forces that are going to shape tomorrow\u27s consumer need, behavior and mood. Thus, the prediction of life events is highly relevant in marketing and sociology. In this paper, we propose a data-driven, real-time method to predict individual life events, using readily available data from smartphones. Our large-scale user study with more than 2000 users shows that our method is able to predict life events with 64.5% higher accuracy, 183.1% better precision and 88.0% higher specificity than a random model on average

MobileAppScrutinator: A Simple yet Efficient Dynamic Analysis Approach for Detecting Privacy Leaks across Mobile OSs

Smartphones, the devices we carry everywhere with us, are being heavily tracked and have undoubtedly become a major threat to our privacy. As "tracking the trackers" has become a necessity, various static and dynamic analysis tools have been developed in the past. However, today, we still lack suitable tools to detect, measure and compare the ongoing tracking across mobile OSs. To this end, we propose MobileAppScrutinator, based on a simple yet efficient dynamic analysis approach, that works on both Android and iOS (the two most popular OSs today). To demonstrate the current trend in tracking, we select 140 most representative Apps available on both Android and iOS AppStores and test them with MobileAppScrutinator. In fact, choosing the same set of apps on both Android and iOS also enables us to compare the ongoing tracking on these two OSs. Finally, we also discuss the effectiveness of privacy safeguards available on Android and iOS. We show that neither Android nor iOS privacy safeguards in their present state are completely satisfying

Understanding workers’ adoption of productivity mobile applications: a fuzzy set qualitative comparative analysis (fsQCA)

Mobile devices such as smartphones and tablets become more present in our lives every day. Most of these devices use the Android operating system (O.S.), becoming the most popular O.S. for mobile devices. For these devices, there is a huge offer of application software that provides answers to users’ different needs. This study aims to analyse how combinations of personality factors, sociodemographic variables and Internet use influence the adoption of productivity mobile apps by workers. To achieve this, a combination of these variables is analysed using fuzzy set Qualitative Comparative Analysis (fsQCA.) that allows us to analyse complex complementarities among factors. The results show the importance of distinct personality traits – extraversion and agreeableness – to understand the adoption of these services. Our study also provides relevant insight for software developers to target segments interested in the use of productivity software in their mobile devices

Experimental Analysis of Popular Smartphone Apps Offering Anonymity, Ephemerality, and End-to-End Encryption

As social networking takes to the mobile world, smartphone apps provide users with ever-changing ways to interact with each other. Over the past couple of years, an increasing number of apps have entered the market offering end-to-end encryption, self-destructing messages, or some degree of anonymity. However, little work thus far has examined the properties they offer. To this end, this paper presents a taxonomy of 18 of these apps: we first look at the features they promise in their appeal to broaden their reach and focus on 8 of the more popular ones. We present a technical evaluation, based on static and dynamic analysis, and identify a number of gaps between the claims and reality of their promises

The right to data protection in the US: the influence of GDPR in the US model

https://www.ester.ee/record=b536097