Exploiting Third-party SDK Sensitive Data Leakage

An Improved Data Leakage Detection System is designed to mitigate the leakage of crucial and sensitive data in a cloud computing environment. Generally, leakage of data in computing system has caused a lot of irreparable damage or catastrophe to various institutions or organizations worldwide. Therefore, this research aims at detecting and preventing any intentional or non-intentional data leakages using dynamic password or key for data decryption security mechanisms. To achieve this the OOADM methodology was adopted. The new system was implemented using ASP.net MVC and Microsoft SQL Server Management Studio as the backend. And by incorporating an Audit trail/Transaction log mechanism, the new system monitors the activities within and outside the computing environment with date and time stamp. Hence, the system can be applied in any environment for the prevention and detection of any data leakage.

The data leaks problem is a security key issue in the worldwide connection, communication and interoperability functions among the huge number of mobile devices, especially for apps where sensitive data are exchanged. In order to tackle this dangerous problem, an innovative and powerful tool, named Android JADAL (JAva DAta Leak), has been developed based on hybrid approach which combines both static and dynamic code analysis techniques. This tool has been validated successfully by means of significant tests carried out on sensitive data leak applications. During the test, the user receives notification when sensible data is caught during the execution of apps. The design and testing activities have been conducted in cooperation with the Mathematics and Computer Science Department of Catania University.

Mobile apps are widely used and often process users’ sensitive data. Many taint analysis tools have been applied to analyze sensitive information flows and report data leaks in apps. These tools require a list of sources (where sensitive data is accessed) as input, and researchers have constructed such lists within the Android platform by identifying Android API methods that allow access to sensitive data. However, app developers may also define methods or use third-party library’s methods for accessing data. It is difficult to collect such source methods, because they are unique to the apps, and there are a large number of third-party libraries available on the market that evolve over time. To address this problem, we propose DAISY, a Dynamic-Analysis-Induced Source discoverY approach for identifying methods that return sensitive information from apps and third-party libraries. Trained on an automatically labeled dataset of methods and their calling context, DAISY identifies sensitive methods in unseen apps. We evaluated DAISY on real-world apps, and the results show that DAISY can achieve an overall precision of 77.9% when reporting the most confident results. Most of the identified sources and leaks cannot be detected by existing technologies.

The Android operating system dominates the smartphone market. Thus, to service the market, the number of Android applications has risen dramatically. These applications are processing a great amount of sensitive data, which could result in various concerns including data leakage and privacy violations. For example, applications may misuse the sensitive data stored on Android devices and violate the privacy of the user. Therefore, it is essential to maintain user privacy and protect sensitive data from leakage. Static data flow analysis approaches are used for analyzing Android applications to uncover security and privacy issues. However, these approaches frequently generate false alarms, given the different challenges created by Android applications, such as inter-component communication (ICC), reflection, and implicit flow. This work presents the DroidRista approach for conducting static data flow analysis on Android applications to detect sensitive data leakage. DroidRista analyzes ICC, reflection, and implicit flow in Android applications. To evaluate the performance of DroidRista, it was tested on three data sets. The results demonstrate improved performance in terms of detecting data leakage compared to existing static data flow analysis approaches.

BackgroundPeople who have migrated or with a language barrier may face significant hurdles in accessing health care. Some apps have been specifically developed to facilitate the dialogue between health care professionals and people who have migrated who have low-level language proficiency or to promote health among people who have migrated.ObjectiveWe conducted a systematic review to investigate development, acceptability, and effectiveness of these types of apps.MethodsWe conducted a search of PubMed, Scopus, and Embase databases. We included all study designs (qualitative, quantitative, mixed) reporting development, evaluation of efficacy, or acceptability of apps facilitating dialogue with a health professional or promoting health for people who have migrated, minorities, or tourists with a language barrier, using any outcome. Two researchers selected the studies independently. We collected general information about the app, information about health literacy and cultural adaptation, information about the development of the app, evidence on acceptability or efficacy, and information on app use. Data were collected by 2 researchers independently and results were reviewed to verify agreement and reported according to PRISMA (Preferred Reporting Items for Systematic Review and Meta-analysis).ResultsPositive results for translation apps included better communication, but with possible limitations, and reduced consultation time. Positive results for health promotion apps included improved quality of life and better management of chronic illnesses.ConclusionsOverall, the apps had good levels of acceptability, though only half had their efficacy evaluated. In those evaluations, the endpoints were mostly related to reported behavior change and knowledge improvement, which is common for evaluations of health promotion programs. In the future, as more health apps are created, it is essential that apps that claim to have a public health objective undergo a rigorous evaluation of their acceptability, efficacy, and actual use. Indicators of outcomes beyond changes in behavior and knowledge should be reported; change in health status or access to care should also be reported. This systematic review has helped us note the characteristics associated with improved acceptability and efficacy, which can be helpful for the development of future apps.

Background – Depression has a profound and complex association with coronary risk, both as an independent risk factor and indirectly through its effects on determinants of coronary risk such as BMI, diet, physical activity, smoking and medication treatment fidelity. Of particular interest is the impact of depression symptoms on initiation of and adherence to behavioural change to reduce coronary risk. Objective – To investigate the association of depression with dietary adherence and weight loss outcomes in a community-based weight loss intervention. Design – A group of 64 overweight (BMI >27), otherwise healthy adults, were recruited and randomised to follow either their usual diet, or an isocaloric diet in which saturated fat was replaced with MUFA to 50% by adding macadamia nuts to the diet. Subjects were assessed for depressive symptoms at baseline and at ten weeks using the Beck Depression Inventory (BDI-II). Both control and intervention groups received advice on National Guidelines for Physical Activity and adhered to the same protocol for food diaries and trial consultations. Anthropometric and clinical measurements were taken at baseline and 10 weeks. Outcomes – There was a significant correlation (R=-0.38, p<0.05) between BDI-II scores at baseline and duration of participation in the trial. Subjects with a baseline BDI≥10 (moderate to severe depression symptoms) were more likely to dropout of the trial before 10 weeks (p<0.001). The BDI-II scores in the intervention (monounsaturated fat) diet group decreased, but increased in the control group over the 10-week period. Univariate analysis of variance confirmed these observations (adjusted R 2 = 0.257, p = 0.01). Body weight remained static over the 10-week period in the intervention group, corresponding to a relative increase in the control group (adjusted R 2 = 0.097, p = 0.064). Conclusion – Depression symptoms have the potential to affect adherence to diet and physical activity-based risk reduction interventions, and may therefore be useful screening criteria.

Federated Learning (FL) has emerged as a suitable option for collaborative, user-centric machine learning while protecting sensitive user data. FL differs significantly from traditional centralized frameworks that collect raw data on a central server. FL focuses on collecting model updates generated locally on edge devices. With traditional FL approaches, data must be downloaded to a central server for model training. In contrast, training occurs on the edge and only model updates are sent. This decentralized framework, unlike traditional FL approaches, is ideal for ubiquitous applications—systems woven seamlessly into everyday surroundings like smart homes, advanced mobile devices, and IoT systems—where sensitive data is constantly and automatically generated. In these systems, user privacy is paramount, and advanced methods must be implemented to ensure privacy and system efficacy. This paper discusses the integration of FL into ubiquitous environments, addressing the relevance, advantages, and disadvantages of this approach. I’d like to focus on privacy-preserving methods, but their preservation against data leakage and adversary attacks adds significant value. There are case studies to show the challenges encountered, lessons learned, and the use of FL within privacy-centric ubiquitous systems. In closing this paper, I summarize the key points and underscore the transformational potential of FL for privacy-sensitive, innovative systems. I also promote further research in this area to support its practical relevance. This paper demonstrates that among the many approaches to privacy within pervasive computing, FL is the only one that is practical and scalable.

Children with communication disorders experience difficulty in one or more areas of articulation and speech, language, fluency, voice and social communication, and they work with speech-language pathologists (SLPs) to improve their communication. With the rise of adoption and use for mobile applications among special education and healthcare service providers, SLPs also have implemented, and for some, contributed to the design of, mobile applications (apps) during clinical practice. However, how these mobile apps are designed and implemented for clinicians to facilitate their clients' communication and learning experiences during therapy remains underinvestigated. This qualitative research study investigates how mobile apps were designed for clinicians to target assessment and intervention goals. Additionally, it focused on how clinicians adopted these apps while integrating therapy techniques to facilitate their clients' learning. Informed by the Research, Practice, and Design for iPad Apps (iRPD) framework and the Consolidated Framework for Implementation Research (CFIR), semi-structured interviews were conducted with 37 licensed pediatric SLPs, including 23 SLPs who have used apps and 14 SLPs who have contributed to the design of their own mobile apps. Two rounds of qualitative coding via template analysis and thematic analysis were then used to analyse client and clinician characteristics, clinical practice, therapy tools, app characteristics, influential factors and app design and use recommendations. Results showed SLPs utilise different genres of assistive, educational and recreational game apps to support children's communication development when working with children who have diverse disorders and therapy needs across different age groups. SLPs who have designed their own apps emphasised the importance of following evidence-based practice, well-researched teaching methods and learning theories. Additionally, multiple financial, sociocultural, political and ethical factors contributed to the design, adoption and implementation of mobile apps during services. By understanding the clinician's app use practices situated in various therapy activities and techniques, we specified a list of design recommendations for app designers who are interested in creating mobile apps for supporting children's speech and language development. By bringing insights from both clinical practitioners as well as those with additional technical design backgrounds, this study contributes to the understanding of clinical practice needs and strategies and will lead to the most optimal app design and adoption practice to support the well-being of children with communication disorders. What is already known on the subject Speech language pathologist (SLPs) implement mobile apps for clients with diverse therapy needs, and their app adoption and use are influenced by multifaceted factors. Although prior studies have reported SLPs' mobile app use, additional information is still needed. For example, the research literature does not include how specific technology is used during therapy practice, or specific details about challenges and needs in implementing and utilising the technology. Additional research also needs to include influential factors (e.g., financial, sociocultural, political, ethical) that are considered when selecting, implementing, assessing and designing an app. The lack of research in these areas directly affects the understanding of clinical mobile technology practices and further hinders clinicians' abilities to advocate for better clinical and design decisions towards identifying and implementing effective mobile apps that facilitate children's communication. What this study adds to existing knowledge This qualitative study is the first known empirical research that interviewed pediatric speech-language pathologists who have used and designed mobile apps for children who receive speech-language therapy across different clinical settings. By investigating experiences from clinician stakeholders to illustrate a holistic overview of app design and development to deployment, this study reported finding on (1) how clinicians use mobile apps to help children to participate in different therapy activities, and (2) a list of recommended design and development guidelines that informs the design and use of mobile apps that best support and motivate children to engage in therapy. What are the potential or actual clinical implications of this work? This study disseminates clinician-reported practices of app design and use with pediatric clients across different speech-language disorders, and identifies gaps and needs for clinicians and researchers who are interested in understanding the role of mobile technology in relationship to human communication and interaction. Additionally, the paper demonstrates that SLPs have instrumental roles rather than passive users in influencing the design and implementation of different genres of mobile apps through evidence-based clinical practice, and call for partnerships across clinicians, special educators and technologists to support children's communication development.

The prevalence of smartphones has promoted the popularity of mobile apps in recent years. Although significant effort has been made to understand mobile app usage, existing studies are based primarily on short-term datasets with a limited time span, e.g., a few months. Therefore, many basic facts about the long-term evolution of mobile app usage are unknown. In this paper, we study how mobile app usage evolves over a long-term period. We first introduce an app usage collection platform named carat, from which we have gathered app usage records of 1,465 users from 2012 to 2017. We then conduct the first study on the long-term evolution processes on a macro-level, i.e., app-category, and micro-level, i.e., individual app. We discover that, on both levels, there is a growth stage enabled by the introduction of new technologies. Then there is a plateau stage caused by high correlations between app categories and a Pareto effect in individual app usage, respectively. Additionally, the evolution of individual app usage undergoes an elimination stage due to fierce intra-category competition. The inter-diversity of app-category and individual app usage exhibits opposing trends: app-category usage assimilates while individual app usage diversifies. Nevertheless, the intra-diversity of both app-category and app usage declines over time. Also, we demonstrate the country barriers of app category usage. We further investigate how different demographics affect the evolutionary processes of app usage. Our study provides useful implications for app developers, market intermediaries, and service providers.

One of the biggest growth areas in e-mental health resources has been the development and use of mobile mental health apps for smartphones and tablet devices. Such apps are being downloaded at increasing rates, but there have been questions about their efficacy and the research methodologies used to examine this. A review of the major app marketplaces, the Apple App Store and Google Play store, was conducted to locate apps claiming to offer a therapeutic treatment for depression and/or anxiety, and have research evidence for their effectiveness, according to their app store descriptions. App store descriptions were also analyzed to determine whether the app had been developed with mental health expert input; whether they had been developed in association with a government body, academic institution, or medical facility; and, whether or not they were free to download. Overall, 3.41% of apps had research to justify their claims of effectiveness, with the majority of that research undertaken by those involved in the development of the app. Other results indicated that 30.38% of shortlisted apps claimed to have expert development input; 20.48% had an affiliation with a government body, academic institution, or medical facility; and, 74.06% were free to download. Future research must consider other methodologies that may facilitate more research being completed on a greater number of apps, and future development needs to incorporate greater levels of input by mental health experts. Ways in which app stores could play a key role in encouraging more scientific research into the effectiveness of the mental health apps they sell are discussed.

In today's digital landscape, ensuring the security of sensitive data and protecting against network intrusions are critical challenges. This research project develops and evaluates a novel context-aware adaptive encryption system that integrates sensitive data detection, network intrusion detection, and dynamic encryption techniques to enhance data security. The proposed system employs deep learning models to identify sensitive information and machine learning algorithms to monitor network activity for potential intrusions. Upon detecting sensitive data or a security threat, the system automatically applies encryption with adjustable strength based on the context, increasing protection in high-risk situations. This approach minimizes unnecessary overhead in low-risk scenarios while maintaining robust security measures. Through simulations using real-world data, the system's effectiveness in accurately detecting sensitive information and network intrusions, as well as its capability to adapt encryption dynamically, is evaluated. The results demonstrate the potential of combining machine learning with adaptive security measures to create a responsive and efficient data protection system.

Logging is an essential software practice that is used by developers to debug, diagnose and audit software systems. Despite the advantages of logging, poor logging practices can potentially leak sensitive data. The problem of data leakage is more severe in applications that run on mobile devices, since these devices carry sensitive identification information ranging from physical device identifiers (e.g., IMEI MAC address) to communications network identifiers (e.g., SIM, IP, Bluetooth ID), and application-specific identifiers related to the location and the users' accounts. This preliminary study explores the impact of logging practices on data leakage of such sensitive information. Particularly, we want to investigate whether log-related statements inserted into an application code could lead to data leakage. While studying logging practices in mobile applications is an active research area, to our knowledge, this is the first study that explores the interplay between logging and security in the context of mobile applications for Android. We propose an approach called MobiLogLeak, an approach that identifies log statements in deployed apps that leak sensitive data. MobiLogLeak relies on taint flow analysis. Among 5,000 Android apps that we studied, we found that 200 apps leak sensitive data through logging.

The prevalence of smartphones has promoted the popularity of mobile apps in recent years. Although significant effort has been made to understand mobile app usage, existing studies are based primarily on short-term datasets with limited time span, e.g., a few months. Therefore, many basic facts about the long-term evolution of mobile app usage are unknown. In this paper, we study how mobile app usage evolves over a long-term period. We first introduce an app usage collection platform named carat, from which we have gathered app usage records of 1,465 users from 2012 to 2017. We then conduct the first study on the long-term evolution processes on a macro-level, i.e., app-category, and micro-level, i.e., individual app. We discover that, on both levels, there is a growth stage enabled by the introduction of new technologies. Then there is a plateau stage caused by high correlations between app categories and a pareto effect in individual app usage, respectively. Additionally, the evolution of individual app usage undergoes an elimination stage due to fierce intra-category competition. Nevertheless, the diverseness of app-category and individual app usage exhibit opposing trends: app-category usage assimilates while individual app usage diversifies. Our study provides useful implications for app developers, market intermediaries, and service providers.

Despite the unequivocal evidence demonstrating the benefits of being physically active, many people do not meet the recommended guidelines of at least 150 minutes of moderate- to vigorous-intensity physical activity per week. This can be changed with the development and implementation of innovative interventions. The use of mobile health (mHealth) technologies has been suggested as a mechanism to offer people innovative health behavior change interventions. This study aims to outline the systematic, theory-driven processes and user testing applied to the development of a smartphone-based physical activity app (SnackApp) to promote participation in a novel physical activity intervention called Snacktivity. The acceptability of the app was explored and reported. Intervention mapping involves a 6-step process, the first 4 of which were presented in this study. These steps were used to develop the SnackApp for use within the Snacktivity intervention. The first step involved a needs assessment, which included composing an expert planning group, patient and public involvement group, and gathering the views of the public on Snacktivity and the public perception of the use of wearable technology to support Snacktivity. This first step aimed to determine the overall purpose of the Snacktivity intervention. Steps 2 to 4 involved determining the intervention objectives, the behavior change theory and techniques on which the intervention is based, and the development of the intervention resources (ie, SnackApp). After the completion of steps 1 to 3 of the intervention mapping process, the SnackApp was developed and linked to a commercial physical activity tracker (Fitbit Versa Lite) for the automated capture of physical activity. SnackApp includes provisions for goal setting, activity planning, and social support. Stage 4 involved users (inactive adults, N=15) testing the SnackApp for 28 days. App engagement (mobile app use analytics) was analyzed to determine app use and to inform the further development of SnackApp. Over the study period (step 4), participants engaged with SnackApp an average of 77 (SD 80) times. On average, participants used the SnackApp for 12.6 (SD 47) minutes per week, with most of the time spent on the SnackApp dashboard and engaging, on average, 14 (SD 12.1) times, lasting 7 to 8 minutes per week. Overall, male participants used the SnackApp more than female participants did. The app rating score was 3.5 (SD 0.6) out of 5, suggesting that SnackApp was rated as fair to good. This study outlines and reports data regarding the development of an innovative mHealth app using a systematic, theory-driven framework. This approach can guide the development of future mHealth programs. User testing of the SnackApp suggested that physically inactive adults will engage with the SnackApp, indicating its applicability of use in the Snacktivity physical activity intervention.

Mobile health (mHealth) interventions show promise in supporting tobacco cessation. However, Black adults who use tobacco products are not well represented in mHealth studies for tobacco cessation, and their preferred features of mHealth apps are not well known. Identifying types of mHealth app features for tobacco cessation preferred by Black adults is critical to developing a culturally adapted app, with increased uptake by the target population. The goal of this study was to explore culturally relevant preferences for features of smoking cessation mHealth apps among Black adults who use tobacco products. A comprehensive list of features of mHealth apps for tobacco cessation was developed based on previous research and a review of existing mHealth literature. Through a content analysis, this list was divided into subgroups and used to develop a focus group guide. We recruited participants from Instagram, a social media platform. Eligible focus group participants included people who reported current use of a tobacco product, identified as being African American or Black, were 21 years old or older, and had access to Wi-Fi or the internet. Participants had to indicate interest in the use of an mHealth app for tobacco cessation. Participants discussed their opinions about different app features, including what features they felt would increase the use of an app by Black adults. Recordings from the focus groups were transcribed and coded deductively and inductively. We conducted a thematic content analysis of the resulting transcripts. Forty adults aged 21-69 (mean 43, SD 13.6) years participated in 8 focus groups. Fifty-seven percent were female, and 88% endorsed current cigarette use. Four central themes that represented app features emerged. (1) Participants wanted representation and inclusivity through personalization and featuring people with similar lived experiences, including representative images and relevant health information. (2) Participants desired the app to feature a diversity of experiences such as testimonials from individuals from different backgrounds rather than solely focusing on racial identity or excessive targeting of the Black community. (3) Participants desired accountability through trusted connections with health care professionals and other support groups within the app, as well as app tracking capability. (4) Encouragement and motivation were more salient incentives than monetary rewards. Black adults who use tobacco products prefer a tobacco cessation app with features that are inclusive, relatable, supportive, and motivating. These findings can serve as the groundwork for the development of an mHealth app that will appeal to Black adults, potentially leading to increased app use, successful cessation, and health equity.