An analysis framework for information loss and privacy leakage on Android applications

With the wide application of machine learning technology, more and more sensitive data were used to develop the machine learning model. Unfortunately, machine learning systems have been shown to be vulnerable to various privacy leakage attacks, such as membership inference attacks and model inversion attacks. These privacy threats break down the confidentiality of the training data; what’s more, the leakage of sensitive privacy will reduce the enthusiasm of data owners for sharing data with the machine learning system. Without enough training data, more seriously, it will hinder the application of machine learning. Therefore, it requires analyzing strategies for privacy risks to help data owners pre-assess the candidate dataset and assist data owners in implementing reasonable privacy control. However, the systematic privacy risk assessment is still absent from the data owner’s perspective.This paper investigates and analyzes machine learning privacy risks to understand the relationship between training data properties and privacy leakage. Based on this recognition, we introduce a privacy risk assessment scheme based on the clustering distance of training data. Our clustering distance-based method can reflect the privacy risk level of a different individual data record. And then, we combine both existing privacy analysis based on data features and our clustering distance-based method to investigate privacy risks systematically. Our experiments showed that our clustering distance method and other set properties are tightly related to privacy leakage. And data owners can pre-assess the privacy risks of candidate datasets before uploading or sharing them to the machine learning model. If needed, decide to re-choose the dataset to reduce privacy risks to an acceptable level.

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.

Privacy risk assessments aim to analyze and quantify the privacy risks associated with new systems. As such, they are critically important in ensuring that adequate privacy protections are built in. However, current methods to quantify privacy risk rely heavily on experienced analysts picking the "correct" risk level on e.g. a five-point scale. In this paper, we argue that a more scientific quantification of privacy risk increases accuracy and reliability and can thus make it easier to build privacy-friendly systems. We discuss how the impact and likelihood of privacy violations can be decomposed and quantified, and stress the importance of meaningful metrics and units of measurement. We suggest a method of quantifying and representing privacy risk that considers a collection of factors as well as a variety of contexts and attacker models. We conclude by identifying some of the major research questions to take this approach further in a variety of application scenarios.

In the modern Internet era the usage of social networks such as Twitter, Instagram and Facebook is constantly increasing. The analysis of this type of data can help us understand interesting social phenomena, because these networks intrinsically capture the new nature of user interactions. Unfortunately, social network data may reveal personal and sensitive information about users, leading to privacy violations. In this paper, we propose a study of privacy risk for social network data. In particular, we empirically analyze a set of privacy attacks on social network data by using the privacy risk assessment framework PRUDEnce. After simulating the attacks on real data, we first analyze how the privacy risk is distributed over the whole population. Then, we study the effect of high-risk users sanitization on some common network metrics.KeywordsPrivacyAttack modelsSocial networks

Users face various privacy risks in smart homes, yet there are limited ways for them to learn about the details of such risks, such as the data practices of smart home devices and their data flow. In this paper, we present Privacy Plumber, a system that enables a user to inspect and explore the privacy "leaks" in their home using an augmented reality tool. Privacy Plumber allows the user to learn and understand the volume of data leaving the home and how that data may affect a user's privacy -- in the same physical context as the devices in question, because we visualize the privacy leaks with augmented reality. Privacy Plumber uses ARP spoofing to gather aggregate network traffic information and presents it through an overlay on top of the device in an smartphone app. The increased transparency aims to help the user make privacy decisions and mend potential privacy leaks, such as instruct Privacy Plumber on what devices to block, on what schedule (i.e., turn off Alexa when sleeping), etc. Our initial user study with six participants demonstrates participants' increased awareness of privacy leaks in smart devices, which further contributes to their privacy decisions (e.g., which devices to block).

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.

Although there has been much research on the leakage of sensitive data in Android applications, most of the existing research focus on how to detect the malware or adware that are intentionally collecting user privacy. There are not much research on analyzing the vulnerabilities of apps that may cause the leakage of privacy. In this paper, we present a vulnerability analyzing method which combines taint analysis and cryptography misuse detection. The four steps of this method are decompile, taint analysis, API call record, cryptography misuse analysis, all of which steps except taint analysis can be executed by the existing tools. We develop a prototype tool PW Exam to analysis how the passwords are handled and if the app is vulnerable to password leakage. Our experiment shows that a third of apps are vulnerable to leak the users' passwords.

We offer a vision for improving mobile device security through the assessment and subsequent visualization of information privacy risk. We note that many security measures from the traditional notebook, desktop, and server domains do not translate well to mobile devices. Moreover, users of mobile devices may often lack security expertise and awareness. Thus, other options must be considered with respect to securing smart phones, tablets, and the like. In particular, we propose a means to assess the information privacy risk for a given mobile device and alert the user when there appears to be significant risk. At its heart, our approach attempts to visualize the privacy disposition of a mobile device, thereby guiding the user to work in a more secure fashion.

Android applications (apps for short) frequently send users' personally identifiable data over IP networks. Previous attempts to address privacy leakage on mobiles by checking whether these sensitive data is off the device. Actually, some benign apps need to collect users' privacy information for many sensitive tasks (e.g., contacts manager, location service, or finance). Since these kinds of data transmissions provide an application's functionality, treating these as privacy leakages are false positives. In this paper, we present Android Privacy Assistant (APA), a practical privacy protection system that reveals privacy disclosure and provides fine-grained privacy information modifications to balance privacy and data usability. APA leverages machine learning to detect privacy leaks by inspecting network traffic, and uses generalization techniques to decrease privacy sensitivity. The evaluation result shows that APA can detect privacy leakage with up to 95% accuracy in our dataset and have almost no overhead on network and battery consumption.

The Quest for Information Privacy in Africa: A Review Essay

Vision-Language Models (VLMs) are increasingly deployed in applications that interpret and generate information from visual and textual inputs. While powerful, these models pose emerging privacy risks. In this paper, we introduce the concept of privacy chains: structured narratives that emerge when adversaries aggregate outputs from VLMs across multiple images, often exposing sensitive information even when the individual outputs are seemingly innocuous. Using LangChain, an open-source orchestration framework, we show how identity-linked data extracted via both benign and targeted prompts can be compiled into detailed timelines of private behavior, significantly amplifying privacy threats. To systematically assess this risk, we develop a privacy leakage pipeline within the Visual Question Answering (VQA) framework and evaluate six open-source VLMs across three tailored datasets: Celebrity, Car, and Tattoo. Our analysis reveals substantial and model-dependent privacy leakage, even from general-purpose queries. To mitigate this threat, we propose ChainShield, a white-box adversarial defense that applies targeted, imperceptible perturbations to images. ChainShield reduces privacy-relevant outputs by redirecting VLM responses toward benign alternatives, while preserving image realism. Our experiments show that ChainShield substantially lowers privacy leakage across models and datasets, effectively disrupting the formation of privacy chains.

Federated Learning (FL) in Consumer Internet of Things (CIoT) encounters significant privacy security threats when collaborative training Machine Learning models using data distributed among numerous smart CIoT devices. This paper’s objective is to evaluate the potential privacy risks associated with FL within CIoT context, specifically through the lens of passive malicious users. We propose an evaluation scheme (MIGAN) for privacy leakage, which incorporates various privacy inference attacks. First, this paper analyzes the characteristics of privacy inference attacks in FL and summarizes privacy security threats to guide the design of the MIGAN evaluation scheme. Then, to fully utilize the privacy information in FL, this paper proposes faster model inversion and white-box membership inference. Integrating these two modules into the MIGAN framework facilitates fast and high-quality privacy data reconstruction. Finally, this paper constructs the evaluation function to measure the correlation between compromised privacy data and users’ training data. Experiments conducted on the MNIST and CelebA datasets reveal that MIGAN’s data privacy reconstruction outperforms comparable methods. Notably, MIGAN quantifies the likelihood of potential privacy leakage at 14.7% for MNIST and 8.3% for CelebA in FL with 30 clients. These results validates the practicality and relevance of MIGAN in evaluating privacy leakage risks.

Over the past few years, we can see the enormous increase of the use of smart phones that are based on android platform. Due to that reason, the mobile application development based on android has boosted the developers' interest. Having countless android application on the market is not an assurance of having users' satisfaction. Some of them do not reach users' expectations due to lack of research on dealing with android-based application requirements. With the fast growing of smart phones and their applications, the needs for better application to be produced are needed. Currently there is no specific research on analyzing the requirements for android applications. This paper proposes a new strategy that uses an activity theory to analyze the requirements of android application. It is then discusses a case study of a Muslim android application and presents the results of its requirements analysis using activity theory. The result of this study shows activity theory as an alternative requirement analysis approach to mobile application development that is based on android operating system.

Today's online marketing industry has widely employed email tracking techniques, such as embedding a tiny tracking pixel, to track email opens of potential customers and measure marketing effectiveness. However, email tracking could allow miscreants to collect metadata information associated with email reading without user awareness and then leverage the information for stealthy surveillance, which has raised serious privacy concerns. In this paper, we present an in-depth and comprehensive study on the privacy implications of email tracking. First, we develop an email tracking system and perform realworld tracking on hundreds of solicited crowdsourcing participants. We estimate the amount of privacy-sensitive information available from email reading, assess privacy risks of information leakage, and demonstrate how easy it is to launch a long-term targeted surveillance attack in real scenarios by simply sending an email with tracking capability. Second, we investigate the prevalence of email tracking through a large-scale measurement, which includes more than 44,000 email samples obtained over a period of seven years. Third, we conduct a user study to understand users' perception of privacy infringement caused by email tracking. Finally, we evaluate existing countermeasures against email tracking and propose guidelines for developing more comprehensive and fine-grained prevention solutions.

Android application security research has been dominated by the malware-detection paradigm, in which either benign or malicious labels are assigned to applications. Although effective in terms of large-scale screening, this binary perspective is not effective to capture the diverse risks brought about by modern Android applications, such as privacy leakage, financial abuse, intrusive tracking, and misuse of system resources. This work provides a structured review of the research efforts on Android app assessment models from a risk-oriented perspective. Rather than proposing new techniques for detection, this review synthesizes the existing literature and organizes prior work through a multi-dimensional taxonomy with respect to risk modelling granularity, risk semantics, evidence sources, learning paradigms, and interpretability. This paper reveals important trends, constraints, and unresolved issues by analysing how current approaches conceptualise and depict risk. The purpose of the article is to outline the shift from malware detection to thorough application risk assessment and to provide a comprehensive reference for future Android security research.