You are presented with multiple paper abstracts, alongside the associated literature review and chapter titles. Given the provided information, you are going to do a literature review. Please generate a coherent literature review that synthesizes the content, draws connections between different works, highlights major findings, and identifies any gaps in the existing research.

Literature Review Title: A Preliminary Survey of Knowledge Discovery on Smartphone Applications (apps): Principles, Techniques and Research Directions for E-health 
Chapter Title:  1) Security and Privacy Concerns of Apps 
1. Abstract of Cited Paper (BIB001):  User interface (UI) interactions are essential to Android applications, as many Activities require UI interactions to be triggered. This kind of UI interactions could also help malicious apps to hide their sensitive behaviors (e.g., sending SMS or getting the user's device ID) from being detected by dynamic analysis tools such as TaintDroid, because simply running the app, but without proper UI interactions, will not lead to the exposure of sensitive behaviors. In this paper we focus on the challenging task of triggering a certain behavior through automated UI interactions. In particular, we propose a hybrid static and dynamic analysis method to reveal UI-based trigger conditions in Android applications. Our method first uses static analysis to extract expected activity switch paths by analyzing both Activity and Function Call Graphs, and then uses dynamic analysis to traverse each UI elements and explore the UI interaction paths towards the sensitive APIs. We implement a prototype system SmartDroid and show that it can automatically and efficiently detect the UI-based trigger conditions required to expose the sensitive behavior of several Android malwares, which otherwise cannot be detected with existing techniques such as TaintDroid. 
2. Abstract of Cited Paper (BIB002):  Although Android's permission system is intended to allow users to make informed decisions about their privacy, it is often ineffective at conveying meaningful, useful information on how a user's privacy might be impacted by using an application. We present an alternate approach to providing users the knowledge needed to make informed decisions about the applications they install. First, we create a knowledge base of mappings between API calls and fine-grained privacy-related behaviors. We then use this knowledge base to produce, through static analysis, high-level behavior profiles of application behavior. We have analyzed almost 80,000 applications to date and have made the resulting behavior profiles available both through an Android application and online. Nearly 1500 users have used this application to date. Based on 2782 pieces of application-specific feedback, we analyze users' opinions about how applications affect their privacy and demonstrate that these profiles have had a substantial impact on their understanding of those applications. We also show the benefit of these profiles in understanding large-scale trends in how applications behave and the implications for user privacy. 
3. Abstract of Cited Paper (BIB003):  Mobile applications (or apps) are rapidly growing in number and variety. These apps provide useful features, but also bring certain privacy and security risks. For example, malicious authors may attach destructive payloads to legitimate apps to create so-called "piggybacked" apps and advertise them in various app markets to infect unsuspecting users. To detect them, existing approaches typically employ pair-wise comparison, which unfortunately has limited scalability. In this paper, we present a fast and scalable approach to detect these apps in existing Android markets. Based on the fact that the attached payload is not an integral part of a given app's primary functionality, we propose a module decoupling technique to partition an app's code into primary and non-primary modules. Also, noticing that piggybacked apps share the same primary modules as the original apps, we develop a feature fingerprint technique to extract various semantic features (from primary modules) and convert them into feature vectors. We then construct a metric space and propose a linearithmic search algorithm (with O(n log n) time complexity) to efficiently and scalably detect piggybacked apps. We have implemented a prototype and used it to study 84,767 apps collected from various Android markets in 2011. Our results show that the processing of these apps takes less than nine hours on a single machine. In addition, among these markets, piggybacked apps range from 0.97% to 2.7% (the official Android Market has 1%). Further investigation shows that they are mainly used to steal ad revenue from the original developers and implant malicious payloads (e.g., for remote bot control). These results demonstrate the effectiveness and scalability of our approach. 
4. Abstract of Cited Paper (BIB004):  Today's smartphone application markets host an ever increasing number of applications. The sheer number of applications makes their review a daunting task. We propose AppsPlayground for Android, a framework that automates the analysis smartphone applications. AppsPlayground integrates multiple components comprising different detection and automatic exploration techniques for this purpose. We evaluated the system using multiple large scale and small scale experiments involving real benign and malicious applications. Our evaluation shows that AppsPlayground is quite effective at automatically detecting privacy leaks and malicious functionality in applications.

Literature Review:
To date, a massive amount of knowledge (and relevant tools) is discussed, as provided by interested people, directly or indirectly helping consumers understand these apps from different perspectives. For example, app developers can use open-source APIs to download apps and their relevant information from the Android market  . After that, they can reverse-engineer apps into Java source codes  . Thus, there are varied studies using these tools to evaluate and help people to understand the nature of these apps, such as a study of different privacy behaviours of apps conducted to help people understand their impact on users. More specifically, for further analysis, researchers have decomposed the codes by which apps are written and extracted their APIs as the designers have used and linked them to privacy-behaviours of users' activities. In their experinmnets, they conducts a static analysis on nearly 80,000 apps available from Android vendors, sent the results to users and asked for their feedback of any potential effect on their adoption patterns BIB003 . In addition, security concerns regarding use of apps is also an important topic to be explored and discussed by researchers. The current major app vendor markets do not provide ideal verification mechanisms to check for malicious behaviour of downloaded apps. Thus, an analysis was conducted on 47 SMS-based Android apps in regard to their potenial to be used for theft of users financially related data and detected there are nearly 90% of apps that have the potential for such issues BIB001 . In addition, Zhou et al. BIB004 find that some apps are designed for stealing advertisement revenues from the app designers and to allow for remote control of victims' smart phones. 2) Network Concepts of Apps In addition, the potential for leak of information allowed by some suspicious apps has been discovered and discussed by their destinations (networks) from around 4,000 apps on the Android market. According to the report by Rastogi, Chen et al. BIB005 , the designers of these apps and information flow from these apps to the problematic domains on the Internet can be seen as network structures. Multiple vulnerable apps use these domains as the destination that provides sensitive information (e.g., GPS information, contacts and IMEI/IMSI number) of their users for unknown purposes. Those researchers conclude that the authors of these free apps use third-party advertisement libraries which in turn causes their app to fall victim to malicious behavior designed to leak the personal private information of their users. 3) Dynamic Analysis In addition to previous work, Zheng, Zhu et al. BIB002 focus on dynamic flow analyses of programs, claiming the static analysis method cannot effectively detect full program behaviours. The authors design a tool for analysing UI (User Interface) interactions behind these apps and effectively detected paths which lead to sensitive information. To sum up, we conclude that security and privacy issues are certainly manifest among these apps on the Android market. However, no specific research focuses on health apps. Thus, a further and complete analysis according to current accepted verification principles is necessary.