Automatic Detection for Privacy Violations in Android Applications

Machine learning is now playing important roles in daily lives, however, the privacy leakages are increasingly getting serious in the meantime. Current solutions to the privacy issues in machine learning, like differential privacy or homomorphic encryption either could only be applied to some specific scenarios or bring huge modification to the model construction, not to mention massive efficiency loss. In this paper, we consider addressing the privacy issue in machine learning from another perspective, without modification to models or severe efficiency loss. We proposed a straightforward privacy preserving machine learning scheme, training machine learning models directly over encrypted data. Ideally, this scheme could provide privacy protection to both training data and test data. We gave it a try by applying order preserving encryption (OPE) to the scheme. We discussed the possibility of using OPE to reveal the order information confidentially for model training. Several OPE algorithms were chosen to utilize the proposed method. Finally, comprehensive experiments were deployed on both synthetic and real datasets. The experiments on real datasets show that the learning performance of several well-known classifiers on before and after encryption changes slightly. The experiments on synthetic datasets show the classifier performance could be ranked according to fidelity and reliability.

Mobile and Internet of Things (IoT) devices are increasingly present in our everyday lives. They are equipped with a wide array of rich sensors and offer ubiquitous connectivity. These properties make the devices perfect candidates for data harvesting and privacy invasion over the Internet. However, these devices are usually locked down by OSes and carriers, making it difficult for the research community---and average users---to understand and mitigate online privacy concerns like disclosure of information to third parties. I argue that improving online privacy requires building systems that identify and control private information transmission. Our key observation is that a privacy dissemination must (by definition) occur over the network, so it is possible to detect and mitigate the leakage in the network traffic. Using this approach, the first challenge is how to detect privacy dissemination in network traffic. To address the problem, I developed a system called ReCon to apply machine learning algorithms to reliably identify Personally Identifiable Information (PII) without knowing the PII values in advance. Second, to quantify and understand the online privacy, I conducted experiments in multiple platforms (mobile applications in Android, iOS, Windows and mobile browsers) and studied how privacy exposure from mobile apps evolved over the range of eight years. Third, I developed a holistic approach to detect media exposure from Android mobile apps. Lastly, I extend such analysis to IoT devices and developed techniques to quantify information exposure---even for encrypted traffic---with semi-automated experiments. The goal of my work is to enable any Internet user to understand and control the private information exposed by mobile and IoT devices over the Internet. My work has enabled substantial progress in the increasingly important area of online privacy, has been cited and used by regulators and investigative journalists, and provides opportunities to improve privacy for individual users and organizations.

Dynamic privacy leakage analysis of Android third-party libraries

In software testing process, test data generation represents an important step for high quality software, even for mobile devices. As proposed in previous works, a potential source for random data generation is represented by the UI layout files that are used for almost all mobile platforms (Android, iOS, Windows Phone/Mobile). This paper continues the previous work and presents a test data generation system based on Android layout files. The test data generator uses DSL files as input and generates test data that conform to several testing principles. The generated test data could be stored in XML files or any format required by the testing frameworks.

Generative agents are a novel AI agent architecture developed in 2023 using LLMs that can generate believable human behaviors. They are of significant importance in social simulation and human-computer interaction. However, there is a lack of research on the privacy and security issues that may arise from its subsequent application in society. Based on the Generative Agent framework, this paper reintroduces a personal information system, and a shared database, reconstructing the memory and reflection systems to realize a Lightweight Generative Agent framework (LGA). To fill the research gap in the field of privacy for Generative Agents, this paper simulates three scenarios with five agents to explore potential privacy and security issues. Based on observed privacy leaks, three defensive strategies are proposed: behavior guideline regulation, Multi-Agents cooperation, and step-back prompts. These strategies have reduced privacy leaks by more than 50.00% on average. For some privacy leaks with special simulation scenarios, it has even reduced privacy leaks by more than 80.00%. Additionally, through supplementary experiments, we demonstrate that LGA achieves better cost-effectiveness compared to the original Generative Agent framework. The analysis of privacy and security issues provides a reference for subsequent research on the safety of generative agents.

A mobile personalized recommendation service satisfies the needs of users and stimulates them to continue to adopt mobile commerce applications. Therefore, how to precisely provide mobile personalized recommendation service is very important for the sustainable development of mobile commerce. However, privacy concerns regarding mobile commerce affect users’ consumption intentions, and also reduce the quality of mobile personalized recommendation services. In order to address this issue and the existing recommendation method problem in the mobile personalized recommendation service, this paper introduces six dimensions of privacy concerns and the relevant contextual information to propose a novel mobile personalized recommendation service based on privacy concerns and context analysis. First, this paper puts forward an intensity measurement method to measure the factors that influence privacy concerns, and then realizes a user-based collaborative filtering recommendation integrated with the intensity of privacy concerns. Second, a context similarity algorithm based on a context ontology-tree is proposed, after which this study realizes a user-based collaborative filtering recommendation integrated with context similarity. Finally, the research produces a hybrid collaborative filtering recommendation integrated with privacy concerns and context information. After experimental verification, the results show that this model can effectively solve the problems of data sparseness and cold starts. More importantly, it can reduce the influence of users’ privacy concerns on the adoption of mobile personalized recommendation services, and promote the sustainable development of mobile commerce.

Privacy is a ubiquitous topic in research nowadays, specially in mobile application scenarios. Users are seldom aware of the privacy hazards they are under when using mobile applications. In this paper we compare two privacy studies and present development strategies that can be employed to favour the awareness of privacy and security issues with a strong focus on location data. Results show that developers should play an increasingly important role in the assurance of privacy since users of mobile apps, although concerned with privacy issues, are not fully prepared for making the right decisions in every situation as a rerun of an old study shows.

The dramatic rise of Android application (app) marketplaces has significantly gained the success of convenience for mobile users. Consequently, with the advantage of numerous Android apps, Android malware seizes the opportunity to steal privacy-sensitive data by pretending to provide functionalities as benign apps do. To distinguish malware from millions of Android apps, researchers have proposed sophisticated static and dynamic analysis tools to automatically detect and classify malicious apps. Most of these tools, however, rely on manual configuration of lists of features based on permissions, sensitive resources, intents, etc., which are difficult to come by. To address this problem, we study real-world Android apps to mine hidden patterns of malware and are able to extract highly sensitive APIs that are widely used in Android malware. We also implement an automated malware detection system, MalPat, to fight against malware and assist Android app marketplaces to address unknown malicious apps. Comprehensive experiments are conducted on our dataset consisting of 31 185 benign apps and 15 336 malware samples. Experimental results show that MalPat is capable of detecting malware with a high $F_1$ score (98.24%) comparing with the state-of-the-art approaches.

Today’s Smartphone operating systems frequently fail to provide users with adequate control and visibility into how the third-party applications use their private data. With TaintDroid realized on Android system, we can detect user’s implicit taint and privacy leakage. But TaintDroid has some inherent defects. To better detect user’s implicit taint and privacy leakage in the Android platform, this paper analyzes implicit taint detection and then proposes an automated detection system based on dynamic taint tracking, called TaintChaser. Monitoring sensitive data with TaintChaser provides informed use of third-party applications for phone users and valuable input for smart-phone security service firms seeking to identify misbehaving applications. TaintChaser can detect behaviors of user’s data leakage in Android applications at a fine granularity level and the system can also analyze and test massive Android software in an automatic way. It uses TaintChaser to automatically analyze 38,268 popular Android applications and finds that 34.41 % of them may leak user’s privacy.

Managing privacy leakage is of great importance in the Android platform. The variety of new user-privacy fraud reveals a new challenge in predicting potential privacy disclosure threats and protecting the privacy inside our pocket. In this paper, we present an analysis framework, called AppLeak, for information loss evaluation, privacy leakage detection, and privacy risk assessment on Android applications. With newly formalized privacy measures, AppLeak can effectively and efficiently support mobile user in identifying privacy risks of specific mobile applications.

As more and more sensitive data is transferred from mobile applications across unsecured channels, it seems imperative that transport layer encryption should be used in any non-trivial instance. Yet, research indicates that many Android developers do not use HTTPS or violate rules which protect user data from man-in-the-middle attacks. This paper seeks to find a root cause of the disparities between theoretical HTTPS usage and in-the-wild implementation of the protocol by looking into Android applications, online resources, and papers published by HTTPS and Android security researchers. From these resources, we extract a set of barrier categories that exist in the path of proper TLS use. These barriers not only include improper developer practices, but also server misconfiguration, lacking documentation, flaws in libraries, the fundamentally complex TLS PKI system, and a lack of consumer understanding of the importance of HTTPS. Following this discussion, we compile a set of potential solutions and patches to better secure Android HTTPS and the TLS/SSL protocol in general. We conclude our survey with gaps in current understanding of the environment and suggestions for further research.

Pollution is a growing issue these days. It is necessary to analyze environment & keep it in check for a for best future as well as healthy living for all. Here we propose an Environment Monitoring System that permit us to watch & check live environment in especially areas through Internet of Things (IOT). IoT supported a real-time environmental monitoring system. It plays a crucial role in today’s world through a huge and pro-tract-ed system of sensor networks concerned to the environment & its parameters. This technique deals with monitoring important environmental conditions like temperature, humidity & CO level using the sensor & then this data is shipped to the web page. This information is often access from anyplace over the internet & then the sensor information is presented as graphical statistics during mobile application. This paper explains & present the implementation & outcome of this environmental system uses the sensors for temperature, humidity, air quality & different environmental parameters of the surrounding space. This data is often used to take remote actions to regulate the conditions. Information is pushed to the distributed storage & android app get to the cloud & present the effect to the end users. The system employs a Node MCU, DHT-11 sensor, MQl35 sensor, which transmits data to WEBPAGE. An Android application is made which accesses the cloud data and displays results to the end users. The sensors interact with microcontroller which processes this information & transmit it over internet. This system is best method for any use in monitoring the environment and handling it because everything is controlled automatically through all the time of the process. The results say everything about the application of this system across different field where it was controlled precisely and effectively which further explains that this system easily makes our work easier because of this automatic monitoring system worries about other unexpected climate issues for world.

Medical mobile applications help improve patient care quality, reduce medical errors, and help physicians make faster, evidence-based decisions in patient care, follow-up and rehabilitation. In this study, it was aimed to determine the usage level of smart phone applications among emergency physicians, to investigate the effects of these applications on diagnosis and treatment, to examine their benefits, to determine the expectations regarding these applications, and to raise awareness on ethical issues. Between 02/25/2018 and 03/25/2018 emergency medicine residents in many provinces of Turkey, emergency medicine specialists, were investigated smartphone app to be used in practice with academics. A statistically significant difference was found between emergency medicine experience and mobile application use (p: 0.020). Most of the physicians surveyed had started using their first mobile apps 4 years ago. It was determined that most of the participants using mobile applications in the medical field were using them as a clinical decision tool. It was observed that those with visual expressions were mostly preferred in the use of mobile applications in the medical field (63.1%). It was determined that 75% of the participants found instant messaging applications useful in the professional field. The biggest problem identified in terms of medical ethics of mobile medical applications; was the thought of a violation of patient privacy. Although technological devices make our lives easier in many ways, examination is of great importance in the patient-physician relationship. Medical mobile applications should be prepared professionally and the content quality should be checked by experts. Due to the nature of current research, it is inevitable that technologies become outdated. There is a need for more comprehensive and up-to-date studies on this subject that appeal to large audiences.

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.

Statistics show that more than 80 applications are installed on each android smartphone. Vulnerability research on Android applications is of critical importance. Recently, academic researchers mainly focus on single bug patterns, while few of them investigate the relations between multiple bugs. Industrial researchers proposed a series of logic exploit chains leveraging multiple logic bugs. However, there is no general model to evaluate the chaining abilities between bugs. This paper presents a formal model to elucidate the relations between multiple bugs in Android applications. To prove the effectiveness of the model, we design and implement a prototype system named AppChainer. AppChainer automatically identifies attack surfaces of Android applications and investigates whether the payloads entering these attack surfaces are “chainable”. Experimental results on 2138 popular Android applications show that AppChainer is effective in identifying and chaining attacker-controllable payloads. It identifies 14467 chainable payloads and constructs 5458 chains both inside a single application and among various applications. The time cost and resource consumption of AppChainer are also acceptable. For each application, the average analysis time is 317 s, and the average memory consumed is 2368 MB. Compared with the most relevant work Jandroid, the experiment results on our custom DroidChainBench show that AppChainer outperforms Jandroid at the precision rate and performs equally with Jandroid at the recall rate.