scholarly journals A first look at Android applications in Google Play related to COVID-19

2021 ◽  
Vol 26 (4) ◽  
Author(s):  
Jordan Samhi ◽  
Kevin Allix ◽  
Tegawendé F. Bissyandé ◽  
Jacques Klein
Keyword(s):  
Mobile Apps ◽  
Contact Tracing ◽  
Location Tracking ◽  
Response Effort ◽  
Sensitive Data ◽  
Android Apps ◽  
Digital World ◽  
Public Health Organizations ◽  
Android Applications ◽  
Google Play

AbstractDue to the convenience of access-on-demand to information and business solutions, mobile apps have become an important asset in the digital world. In the context of the COVID-19 pandemic, app developers have joined the response effort in various ways by releasing apps that target different user bases (e.g., all citizens or journalists), offer different services (e.g., location tracking or diagnostic-aid), provide generic or specialized information, etc. While many apps have raised some concerns by spreading misinformation or even malware, the literature does not yet provide a clear landscape of the different apps that were developed. In this study, we focus on the Android ecosystem and investigate Covid-related Android apps. In a best-effort scenario, we attempt to systematically identify all relevant apps and study their characteristics with the objective to provide a first taxonomy of Covid-related apps, broadening the relevance beyond the implementation of contact tracing. Overall, our study yields a number of empirical insights that contribute to enlarge the knowledge on Covid-related apps: (1) Developer communities contributed rapidly to the COVID-19, with dedicated apps released as early as January 2020; (2) Covid-related apps deliver digital tools to users (e.g., health diaries), serve to broadcast information to users (e.g., spread statistics), and collect data from users (e.g., for tracing); (3) Covid-related apps are less complex than standard apps; (4) they generally do not seem to leak sensitive data; (5) in the majority of cases, Covid-related apps are released by entities with past experience on the market, mostly official government entities or public health organizations.

scholarly journals Factors that Influence the Android Apps Permissions

2016 ◽  
Vol 1 (2) ◽  
pp. 59
Author(s):  
Normi Sham Awang Abu Bakar ◽  
Iqram Mahmud
Keyword(s):  
Not Given ◽  
Sensitive Data ◽  
Security Issues ◽  
Android Apps ◽  
Android Applications ◽  
Android Market ◽  
Malicious Apps ◽  
The Right ◽  
User Ratings ◽  
Average User

The Android Market is the official (and primary) storefor Android applications. The Market provides users with average user ratings, user reviews, descriptions, screenshots,and permissions to help them select applications. Generally, prior to installation of the apps, users need to agree on the permissions requested by the apps, they are not given any other option. Essentially, users may not aware on some security issues that may arise from the permissions. Some apps request the right to manipulate sensitive data, such as GPS location, photos, calendar, contact, email and files. In this paper, we explain the sources of sensitive data, what the malicious apps can do to the data, and apply the empirical software engineering analysis to find the factors that could potentially influence the permissions in Android apps. In addition, we also highlight top ten most implemented permissions in Android apps and also analyse the permissions for the apps categories in Android.

The Role of Trust in Covid Tracing Apps: Estonian Case Study (Preprint)

2021 ◽  
Author(s):  
Sonia Sousa ◽  
Tiina Kalju
Keyword(s):  
Minority Groups ◽  
Mobile Apps ◽  
Control Measures ◽  
Security And Privacy ◽  
Contact Tracing ◽  
P Value ◽  
Sensitive Data ◽  
Convenience Sample ◽  
The World ◽  
Wide Availability

BACKGROUND The COVID-19 pandemic has caused changes on how we use technology across the world, both socially and economically. Due to the urgency and severity of the crisis different virus control measures were explored. One of the means how technology could help in this situation was by helping trace the contacts of people to prevent the spread of the disease. Many governments and public health authorities across the world have launched a number of contact tracing mobile apps (CTA). By the end of 2020, there are more than 50 contact tracing apps in both Google Play and iOS App Store [1]. Despite the wide availability, the download rates are low and usage rates are even lower [2][3]. There could be many reasons why the adoption is so low, but most certainly one variable that has been overlooked is the level of trust that potential users need to feel comfortable using an app. In Estonia, the CTA named HOIA has been developed as a means of digital contact tracing. By the middle of January 2021, there have been approximately 250 000 downloads but only 1763 (around 4,7% of all COVID-19 positive in Estonia by that time) people have registered as being tested COVID-19 positive [4]. It shows that HOIA has not proved to be efficient means to reduce the spread of the pandemic. Modeling evidence suggests that in order to be effective, the use of contact tracing apps would need to be very high, at least 80% of smartphone users to stop the pandemic [5]. 40% of Estonian people who don’t have HOIA do not believe that HOIA is effective and does what is promised. The concern about security and privacy was in the second place [6]. OBJECTIVE The goal of this study was to assess Estonian's trust towards the HOIA app and what has caused the shortage in trust. Namely, assess how much Estonians trust Covid-19 contact tracing app HOIA and what aspects are perceived as distrust by them. The study contributes to designers' understanding and awareness of designing trustworthy technology. METHODS The study comprised of measuring trust in HOIA CTA application using human-computer Trust psychometric scale [22]. A convenience sample was used in data collection, this includes all potential HOIA among the Estonian population. RESULTS Results indicate significant positive correlations between participants' trust towards the Estonian COVID tracing application (HOIA) and their perceptions of risk (p-value 0.000), competency (P-value 0.000), Benevolence (P-value=0.025), and reciprocity (P-value 0.015). CONCLUSIONS With the COVID-19 crisis, the new phenomenon of contact tracing apps was introduced to fight against the pandemic. CTAs were hoped to be a technological breakthrough to decrease the spread of the virus. However, this has not happened around the world. The same has happened in Estonia and evidence shows, that one of the reasons could be the low level of trust. The results of the study confirm, that trust in HOIA among Estonian habitants does affect their predisposition to use and indicated that participants do not believe HOIA is able to fulfill the main goal and decrease the spread of the virus. The result of this work is not only limited to HOIA but can be implemented by other CTAs as well. The results of this study contribute to designers' understanding and awareness of designing trustworthy technology. Eventually helps to provide design recommendations that ensure trustworthiness in the CTAs AI ability to use highly sensitive data and serve society. Regarding the limitations of this study, the survey was able to gather insight about the perceptions of HOIA, was enough to make a statistical generalization about the users’ perception and usage habits but more data needs to be collected if the intention is to generalize the results to the whole population of Estonia. Also, we should pay attention to the different minority groups to reach a valid conclusion. CLINICALTRIAL no trial registration.

scholarly journals A Research on Strategy Selling in Mobile Applications (With Special Reference to Instructional Applications)

2019 ◽  
Vol 8 (12S) ◽  
pp. 522-524
Keyword(s):  
Special Reference ◽  
Mobile Applications ◽  
Building Block ◽  
Mobile Apps ◽  
Scarce Resources ◽  
Digital World ◽  
Area Unit ◽  
A Company ◽  
Google Play

The selling strategy may be a arrange developed for achieving the selling objectives of the organization. It provides a layout for attaining their selling objectives simply. The strategy is that the building block of a selling arrange. A selling strategy helps a company to arrange their scarce resources on the most effective opportunities so that they will increase their sales. Meantime mobile applications play an important role during this digital world. They increase the users by adapting the selling methods to extend their main objective (profit). During this study the training mobile apps area unit taken, to search out however these app developers area unit victimisation the MS to boost their business and increasing the numbers of users for these mobile apps. These applications area unit offered in google play store, apple store, black berry store to transfer the applications in their mobile. This study reveals that the developers victimisation totally different strategy to sustain their applications within the market

scholarly journals Technology, Privacy, and User Opinions of COVID-19 Mobile Apps for Contact Tracing: Systematic Search and Content Analysis

10.2196/23467 ◽  
2021 ◽  
Vol 23 (2) ◽  
pp. e23467
Author(s):  
Mahmoud Elkhodr ◽  
Omar Mubin ◽  
Zainab Iftikhar ◽  
Maleeha Masood ◽  
Belal Alsinglawi ◽  
...  
Keyword(s):  
New Zealand ◽  
Personal Information ◽  
Mobile Apps ◽  
The United States ◽  
Contact Tracing ◽  
Policy Makers ◽  
App Store ◽  
Technical Issues ◽  
Google Play ◽  
Privacy Issues

Background Many countries across the globe have released their own COVID-19 contact tracing apps. This has resulted in the proliferation of several apps that used a variety of technologies. With the absence of a standardized approach used by the authorities, policy makers, and developers, many of these apps were unique. Therefore, they varied by function and the underlying technology used for contact tracing and infection reporting. Objective The goal of this study was to analyze most of the COVID-19 contact tracing apps in use today. Beyond investigating the privacy features, design, and implications of these apps, this research examined the underlying technologies used in contact tracing apps. It also attempted to provide some insights into their level of penetration and to gauge their public reception. This research also investigated the data collection, reporting, retention, and destruction procedures used by each of the apps under review. Methods This research study evaluated 13 apps corresponding to 10 countries based on the underlying technology used. The inclusion criteria ensured that most COVID-19-declared epicenters (ie, countries) were included in the sample, such as Italy. The evaluated apps also included countries that did relatively well in controlling the outbreak of COVID-19, such as Singapore. Informational and unofficial contact tracing apps were excluded from this study. A total of 30,000 reviews corresponding to the 13 apps were scraped from app store webpages and analyzed. Results This study identified seven distinct technologies used by COVID-19 tracing apps and 13 distinct apps. The United States was reported to have released the most contact tracing apps, followed by Italy. Bluetooth was the most frequently used underlying technology, employed by seven apps, whereas three apps used GPS. The Norwegian, Singaporean, Georgian, and New Zealand apps were among those that collected the most personal information from users, whereas some apps, such as the Swiss app and the Italian (Immuni) app, did not collect any user information. The observed minimum amount of time implemented for most of the apps with regard to data destruction was 14 days, while the Georgian app retained records for 3 years. No significant battery drainage issue was reported for most of the apps. Interestingly, only about 2% of the reviewers expressed concerns about their privacy across all apps. The number and frequency of technical issues reported on the Apple App Store were significantly more than those reported on Google Play; the highest was with the New Zealand app, with 27% of the reviewers reporting technical difficulties (ie, 10% out of 27% scraped reviews reported that the app did not work). The Norwegian, Swiss, and US (PathCheck) apps had the least reported technical issues, sitting at just below 10%. In terms of usability, many apps, such as those from Singapore, Australia, and Switzerland, did not provide the users with an option to sign out from their apps. Conclusions This article highlighted the fact that COVID-19 contact tracing apps are still facing many obstacles toward their widespread and public acceptance. The main challenges are related to the technical, usability, and privacy issues or to the requirements reported by some users.

scholarly journals A Security-Related Reputation Scheme of Android Apps Based on NLP Analysis of Comments

2020 ◽  
Author(s):  
Franklin Tchakounté ◽  
Athanase Esdras Yera Pagore ◽  
Marcellin Atemkeng ◽  
Jean Claude Kamgang
Keyword(s):  
Natural Language Processing ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Text Analysis ◽  
Positive Functional ◽  
Fine Grained ◽  
Android Apps ◽  
Depth Analysis ◽  
Android Applications ◽  
Google Play

Comments are exploited by product vendors to measure satisfaction of consumers. With the advent of Natural Language Processing (NLP), comments on Google Play can be processed to extract knowledge on applications such as their reputation. Proposals in that direction are either informal or interested merely on functionality. Unlike, this work aims to determine reputation of Android applications in terms of confidentiality, integrity, availability and authentication (CIAA). This work proposes a model of assessing app reputation relying on sentiment analysis and text analysis of comments. While assuming that comments are reliable, we collect Google Play applications subject to comments which include security keywords. An in-depth analysis of keywords based on Naive Bayes classification is made to provide polarity of any comment. Based on comment polarity, reputation is evaluated for the whole application. Experiments made on real applications including dozens to billions of comments, reveal that developers lack to make efforts to guarantee CIAA services. A fine-grained analysis shows that not security reputed applications can be reputed in specific CIAA services. Results also show that applications with negative security polarities display in general positive functional polarities. This result suggests that security checking should include careful comment analysis to improve security of applications.

scholarly journals Assessment of the Fairness of Privacy Policies of Mobile Health Apps: Scale Development and Evaluation in Cancer Apps (Preprint)

2019 ◽  
Author(s):  
Jaime Benjumea ◽  
Jorge Ropero ◽  
Octavio Rivera-Romero ◽  
Enrique Dorronzoro-Zubiete ◽  
Alejandro Carrasco
Keyword(s):  
Mobile Health ◽  
Mobile Apps ◽  
Privacy Policies ◽  
Sensitive Data ◽  
General Data Protection Regulation ◽  
Android Apps ◽  
Mobile Health Apps ◽  
General Data ◽  
Adjusted Scores ◽  
Mhealth Apps

BACKGROUND Cancer patients are increasingly using mobile health (mHealth) apps to take control of their health. Many studies have explored their efficiency, content, usability, and adherence; however, these apps have created a new set of privacy challenges, as they store personal and sensitive data. OBJECTIVE The purpose of this study was to refine and evaluate a scale based on the General Data Protection Regulation and assess the fairness of privacy policies of mHealth apps. METHODS Based on the experience gained from our previous work, we redefined some of the items and scores of our privacy scale. Using the new version of our scale, we conducted a case study in which we analyzed the privacy policies of cancer Android apps. A systematic search of cancer mobile apps was performed in the Spanish version of the Google Play website. RESULTS The redefinition of certain items reduced discrepancies between reviewers. Thus, use of the scale was made easier, not only for the reviewers but also for any other potential users of our scale. Assessment of the privacy policies revealed that 29% (9/31) of the apps included in the study did not have a privacy policy, 32% (10/31) had a score over 50 out of a maximum of 100 points, and 39% (12/31) scored fewer than 50 points. CONCLUSIONS In this paper, we present a scale for the assessment of mHealth apps that is an improved version of our previous scale with adjusted scores. The results showed a lack of fairness in the mHealth app privacy policies that we examined, and the scale provides developers with a tool to evaluate their privacy policies.

scholarly journals Automated Bidirectional Languages Localization Testing for Android Apps with Rich GUI

2016 ◽  
Vol 2016 ◽  
pp. 1-13 ◽  
Author(s):  
Aiman M. Ayyal Awwad ◽  
Wolfgang Slany
Keyword(s):  
Mobile Applications ◽  
Mobile Apps ◽  
Arabic Language ◽  
Automatic Testing ◽  
High Quality ◽  
Android Apps ◽  
Culturally Congruent ◽  
Android Applications

Mobile apps are everywhere. The release of apps on a worldwide scale requires them to be made available in many languages, including bidirectional languages. Developers and translators are usually different persons. While automatic testing by itself is important in general in order to be able to develop high quality software, such automatic tests become absolutely essential when developers that do not possess enough knowledge about right-to-left languages need to maintain code that is written for bidirectional languages. A few bidirectional localization tests of mobile applications exist. However, their functionality is limited since they only cover translations and adoption of locales. In this paper we present our approach for automating the bidirectional localization testing for Android applications with a complete consideration for BiDi-languages issues. The objective is to check for any localization defects in the product. The proposed methods are used to test issues of bidirectional apps in general and specifically for the Arabic language. The results show that the methods are able to effectively reveal deficiencies in the app’s design, ensure that the localized app matches all expectations of local users, and guarantee that the product is culturally congruent to local conventions.

scholarly journals A State of Art Survey for Understanding Malware Detection Approaches in Android Operating System

2021 ◽  
pp. 44-60
Author(s):  
Suhaib Jasim Hamdi ◽  
Naaman Omar ◽  
Adel AL-zebari ◽  
Karwan Jameel Merceedi ◽  
Abdulraheem Jamil Ahmed ◽  
...  
Keyword(s):  
Machine Learning ◽  
Operating System ◽  
Malware Detection ◽  
Personal Digital Assistants ◽  
Android Malware ◽  
Detection Techniques ◽  
Android Apps ◽  
Digital World ◽  
Android Malware Detection ◽  
Android Applications

Mobile malware is malicious software that targets mobile phones or wireless-enabled Personal digital assistants (PDA), by causing the collapse of the system and loss or leakage of confidential information. As wireless phones and PDA networks have become more and more common and have grown in complexity, it has become increasingly difficult to ensure their safety and security against electronic attacks in the form of viruses or other malware. Android is now the world's most popular OS. More and more malware assaults are taking place in Android applications. Many security detection techniques based on Android Apps are now available. Android applications are developing rapidly across the mobile ecosystem, but Android malware is also emerging in an endless stream. Many researchers have studied the problem of Android malware detection and have put forward theories and methods from different perspectives. Existing research suggests that machine learning is an effective and promising way to detect Android malware. Notwithstanding, there exist reviews that have surveyed different issues related to Android malware detection based on machine learning. The open environmental feature of the Android environment has given Android an extensive appeal in recent years. The growing number of mobile devices, they are incorporated in many aspects of our everyday lives. In today’s digital world most of the anti-malware tools are signature based which is ineffective to detect advanced unknown malware viz. Android OS, which is the most prevalent operating system (OS), has enjoyed immense popularity for smart phones over the past few years. Seizing this opportunity, cybercrime will occur in the form of piracy and malware. Traditional detection does not suffice to combat newly created advanced malware. So, there is a need for smart malware detection systems to reduce malicious activities risk. The present paper includes a thorough comparison that summarizes and analyses the various detection techniques.

scholarly journals Android Apps Security Assessment using Sentiment Analysis Techniques: Comparative Study

2021 ◽  
Vol 15 (24) ◽  
pp. 123-133
Author(s):  
Abeer Aljumah ◽  
Amjad Altuwijri ◽  
Thekra Alsuhaibani ◽  
Afef Selmi ◽  
Nada Alruhaily
Keyword(s):  
Comparative Study ◽  
Sentiment Analysis ◽  
Security Assessment ◽  
Sensitive Data ◽  
User Reviews ◽  
Application Security ◽  
Analysis Techniques ◽  
Android Apps ◽  
Fake Reviews ◽  
Google Play

Considering that application security is an important aspect, especially nowadays with the increase in technology and the number of fraudsters. It should be noted that determining the security of an application is a difficult task, especially since most fraudsters have become skilled and professional at manipulating people and stealing their sensitive data. Therefore, we pay attention to trying to spot insecurity apps, by analyzing user feedback on the Google Play platform and using sentiment analysis to determine the apps level of security. As it is known, user reviews reflect their experiments and experiences in addition to their feelings and satisfaction with the application or not. But unfortunately, not all of these reviews are real, and as is known, the fake reviews do not reflect the sincerity of feelings, so we have been keen in our work to filter the reviews to be the result is accurate and correct. This study is useful for both users wanting to install android apps and for developers interested in app optimization.

scholarly journals RAPID-Risk Assessment of Android Permission and Application Programming Interface (API) Call for Android Botnet

2018 ◽  
Vol 7 (4.15) ◽  
pp. 49 ◽  
Author(s):  
Zubaile Abdullah ◽  
Madihah Mohd Saudi
Keyword(s):  
Risk Assessment ◽  
Application Programming Interface ◽  
Assessment Method ◽  
Risk Level ◽  
Android Apps ◽  
Risk Assessment Method ◽  
Application Programming ◽  
Android Applications ◽  
Android Permission ◽  
Google Play

Android applications may pose risks to smartphone users. Most of the current security countermeasures for detecting dangerous apps show some weaknesses. In this paper, a risk assessment method is proposed to evaluate the risk level of Android apps in terms of confidentiality (privacy), integrity (financial) and availability (system). The proposed research performs mathematical analysis of an app and returns a single easy to understand evaluation of the app’s risk level (i.e., Very Low, Low, Moderate, High, and Very High). These schemes have been tested on 2488 samples coming from Google Play and Android botnet dataset. The results show a good accuracy in both identifying the botnet apps and in terms of risk level. 

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