22,131 research outputs found
Managing Stored Data For Mobile Apps: Survey Of Apps And Case Study
Stored data is a critical component of any application. The stored data component of mobile applications (apps) presents special considerations. This paper examines the management of stored data for mobile apps. It identifies three types of mobile apps and describes the stored data characteristics of each type. It presents decision factors for selecting a data storage approach for a mobile app and the impact of the factors on the usability of the app. The paper surveys over 70 apps in a specific domain (that of walking the Camino de Santiago in Spain) to examine their data storage characteristics. Finally the paper presents a case study of the development of one app in this domain (eCamino). The paper concludes that in the domain examined the data storage approach selected for a mobile app depends on the characteristics of the situation in which the app will be used
A Forensically Sound Adversary Model for Mobile Devices
In this paper, we propose an adversary model to facilitate forensic
investigations of mobile devices (e.g. Android, iOS and Windows smartphones)
that can be readily adapted to the latest mobile device technologies. This is
essential given the ongoing and rapidly changing nature of mobile device
technologies. An integral principle and significant constraint upon forensic
practitioners is that of forensic soundness. Our adversary model specifically
considers and integrates the constraints of forensic soundness on the
adversary, in our case, a forensic practitioner. One construction of the
adversary model is an evidence collection and analysis methodology for Android
devices. Using the methodology with six popular cloud apps, we were successful
in extracting various information of forensic interest in both the external and
internal storage of the mobile device
A look into the information your smartphone leaks
The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Some smartphone applications (apps) pose a risk to users’ personal information. Events of apps leaking information stored in smartphones illustrate the danger that they present. In this paper, we investigate the amount of personal information leaked during the installation and use of apps when accessing the Internet. We have opted for the implementation of a Man-in-the-Middle proxy to intercept the network traffic generated by 20 popular free apps installed on different smartphones of distinctive vendors. This work describes the technical considerations and requirements for the deployment of the monitoring WiFi network employed during the conducted experiments. The presented results show that numerous mobile and personal unique identifiers, along with personal information are leaked by several of the evaluated apps, commonly during the installation process
A Mobile Money Solution for Illiterate Users
Existing mobile money platforms have text based interfaces and target literate people. Illiterate people, without the assistance of literate individuals, cannot use such platforms. Applying user-centered requirements gathered in an Ethiopian context, this paper presents the design and development of a mobile money solution that targets illiterate people. Particular emphasis is given to how illiterate users deal with cash money in their everyday life and how such practices can be mapped into financial technology design. Given the ubiquity of mobile telephony in Africa, our solution is based on the widely available, relatively inexpensive and open source Android mobile web platform. The proposed system enables illiterate individuals to count money bills, while providing the facility to accept and make payments. In so doing, we provide an example of how a pervasive technology such as smartphones can empower a hitherto often neglected user category of illiterate users
"If You Can't Beat them, Join them": A Usability Approach to Interdependent Privacy in Cloud Apps
Cloud storage services, like Dropbox and Google Drive, have growing
ecosystems of 3rd party apps that are designed to work with users' cloud files.
Such apps often request full access to users' files, including files shared
with collaborators. Hence, whenever a user grants access to a new vendor, she
is inflicting a privacy loss on herself and on her collaborators too. Based on
analyzing a real dataset of 183 Google Drive users and 131 third party apps, we
discover that collaborators inflict a privacy loss which is at least 39% higher
than what users themselves cause. We take a step toward minimizing this loss by
introducing the concept of History-based decisions. Simply put, users are
informed at decision time about the vendors which have been previously granted
access to their data. Thus, they can reduce their privacy loss by not
installing apps from new vendors whenever possible. Next, we realize this
concept by introducing a new privacy indicator, which can be integrated within
the cloud apps' authorization interface. Via a web experiment with 141
participants recruited from CrowdFlower, we show that our privacy indicator can
significantly increase the user's likelihood of choosing the app that minimizes
her privacy loss. Finally, we explore the network effect of History-based
decisions via a simulation on top of large collaboration networks. We
demonstrate that adopting such a decision-making process is capable of reducing
the growth of users' privacy loss by 70% in a Google Drive-based network and by
40% in an author collaboration network. This is despite the fact that we
neither assume that users cooperate nor that they exhibit altruistic behavior.
To our knowledge, our work is the first to provide quantifiable evidence of the
privacy risk that collaborators pose in cloud apps. We are also the first to
mitigate this problem via a usable privacy approach.Comment: Authors' extended version of the paper published at CODASPY 201
Android Malware Clustering through Malicious Payload Mining
Clustering has been well studied for desktop malware analysis as an effective
triage method. Conventional similarity-based clustering techniques, however,
cannot be immediately applied to Android malware analysis due to the excessive
use of third-party libraries in Android application development and the
widespread use of repackaging in malware development. We design and implement
an Android malware clustering system through iterative mining of malicious
payload and checking whether malware samples share the same version of
malicious payload. Our system utilizes a hierarchical clustering technique and
an efficient bit-vector format to represent Android apps. Experimental results
demonstrate that our clustering approach achieves precision of 0.90 and recall
of 0.75 for Android Genome malware dataset, and average precision of 0.98 and
recall of 0.96 with respect to manually verified ground-truth.Comment: Proceedings of the 20th International Symposium on Research in
Attacks, Intrusions and Defenses (RAID 2017
Investigating UI displacements in an Adaptive Mobile Homescreen
The authors present a system that adapts application shortcuts (apps) on the homescreen of an Android smartphone, and investigate the effect of UI displacements that are caused by the choice of adaptive model and the order of apps in the homescreen layout. They define UI displacements to be the distance that items move between adaptations, and they use this as a measure of stability. An experiment with 12 participants is performed to evaluate the impact of UI displacements on the homescreen. To make the distribution of apps in the experiment task less contrived, naturally generated data from a pilot study is used. The authors’ results show that selection time is correlated to the magnitude of the previous UI displacement. Additionally, selection time and subjective rating improve significantly when the model is easy to understand and an alphabetical order is used, conditions that increase stability. However, rank order is preferred when the model updates frequently and is less easy to understand. The authors present their approach to adapting apps on the homescreen, and initial insights into UI displacements
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