Target App Research Articles

App review driven collaborative bug finding

Software development teams generally welcome any effort to expose bugs in their code base. In this work, we build on the hypothesis that mobile apps from the same category (e.g., two web browser apps) may be affected by similar bugs in their evolution process. It is therefore possible to transfer the experience of one historical app to quickly find bugs in its new counterparts. This has been referred to as collaborative bug finding in the literature. Our novelty is that we guide the bug finding process by considering that existing bugs have been hinted within app reviews. Concretely, we design the BugRMSys approach to recommend bug reports for a target app by matching historical bug reports from apps in the same category with user app reviews of the target app. We experimentally show that this approach enables us to quickly expose and report dozens of bugs for targeted apps such as Brave (web browser app). BugRMSys ’s implementation relies on DistilBERT to produce natural language text embeddings. Our pipeline considers similarities between bug reports and app reviews to identify relevant bugs. We then focus on the app review as well as potential reproduction steps in the historical bug report (from a same-category app) to reproduce the bugs. Overall, after applying BugRMSys to six popular apps, we were able to identify, reproduce and report 20 new bugs: among these, 9 reports have been already triaged, 6 were confirmed, and 4 have been fixed by official development teams.

Directing smartphone use through the self-nudge app one sec

Research suggests various associations of smartphone use with a range of physical, psychological, and performance dimensions. Here, we test one sec, a self-nudging app that is installed by the user in order to reduce the mindless use of selected target apps on the smartphone. When users attempt to open a target app of their choice, one sec interferes with a pop-up, which combines a deliberation message, friction by a short waiting time, and the option to dismiss opening the target app. In a field-experiment, we collected behavioral user data from 280 participants over 6 wk, and conducted two surveys before and after the intervention span. one sec reduced the usage of target apps in two ways. First, on average 36% of the times participants attempted opening a target app, they closed that app again after one sec interfered. Second, over the course of 6wk, users attempted to open target apps 37% less than in the first week. In sum, one sec decreased users' actual opening of target apps by 57% after six consecutive weeks. Afterward, participants also reported spending less time with their apps and indicated increased satisfaction with their consumption. To disentangle one sec's effects, we tested its three psychological features in a preregistered online experiment (N = 500) that measured the consumption of real and viral social media video clips. We found that providing the additional option to dismiss the consumption attempt had the strongest effect. While the friction by time delay also reduced consumption instances, the deliberation message was not effective.

A Risk Estimation Mechanism for Android Apps based on Hybrid Analysis

Mobile apps represent essential tools in our daily routines, supporting us in almost every task. However, this assistance might imply a high cost in terms of privacy. Indeed, mobile apps gather a massive amount of data about individuals (e.g., users’ profiles and habits) and their devices (e.g., locations), where not all are strictly needed for app execution. According to privacy laws, apps’ providers must inform end-users on adopted data usage practices (e.g., which data are collected and for which purpose). Unfortunately, understanding these practices is a complex task for average end-users. The result is that they install apps without understanding their privacy implications. To support users in making more privacy-aware decisions on app usage, we propose a risk estimation approach based on an analysis of the app’s code. This analysis adopts a hybrid strategy, exploiting static and dynamic code analyses. Static analysis aims at discovering which personal data an app is collecting to determine whether the target app is asking more than required. This gives the first estimation of the app’s risk level. In addition, we also perform a dynamic analysis of the target app’s code. This further analysis helps determining whether the collected personal data is consumed locally on the mobile device or sent out to external services. If this happens, the risk level has to be increased, as personal data are more exposed. To prove the proposal’s effectiveness, we run several experiments involving different groups of participants. The obtained accuracy results are promising and outperform those obtained with static analysis only.

A Review of CRISPR Cas9 for Alzheimer’s Disease: Treatment Strategies and Could target APOE e4, APP, and PSEN-1 Gene using CRISPR cas9 Prevent the Patient from Alzheimer’s Disease?

A Review of CRISPR Cas9 for Alzheimer’s Disease: Treatment Strategies and Could target APOE e4, APP, and PSEN-1 Gene using CRISPR cas9 Prevent the Patient from Alzheimer’s Disease? BACKGROUND: Alzheimer’s disease is a neurodegenerative disorder characterized by the formation of β-amyloid plaques and neurofibrillary tangles from hyperphosphorylated tau. Several studies suggest that targeting the deletion of the APOE e4, PSEN-1, and APP will reduce tau phosphorylation and Aβ protein accumulation, a crucial hypothesis for the causation of Alzheimer’s disease. APOE e4, PSEN-1, and APP with genome editing Clustered Regular interspersed Short Palindromic Repeats-CRISPR-related (CRISPR/Cas9) are thought to have therapeutic promise for Alzheimer’s disease.AIM: The purpose of this study was to determine whether targeting APOE e4, PSEN-1, and APP using CRISPR/Cas9 is an effective therapeutic and whether it has a long-term effect on Alzheimer’s disease.METHODS: The method used in this study summarized articles by examining the titles and abstracts of specific specified keywords. In this situation, the author picked the title and abstract that matched PubMed, Google Scholar, Science Direct, Cochrane, and the Frontiers in Neuroscience; this was followed by checking to see whether the paper was available in full-text. Eventually, the researcher will study the entire article to decide if it is valuable and relevant to the issue.RESULTS: CRISPR/Cas9 deletion of APOE e4, PSEN-1, and APP in induced pluripotent stem cells (iPSC’s) and g2576 mice as APP mutant models reduce tau phosphorylation and Aβ protein accumulation from neurofibrillary tangles and prevent cell death, vascular damage, and dementia. Furthermore, CRISPR/Cas9 deletion in APOE e4, PSEN-1, and APP improved neuronal cell resilience to oxidative stress and inflammation.CONCLUSION: APOE e4, PSEN-1, and APP deletion by genome editing CRISPR/Cas9 is effective to reduce tau phosphorylation and Aβ protein accumulation from neurofibrillary tangles, cell death, vascular damage, and dementia. However, further research is needed to determine the side effects and safety of its use.

Perceived Barriers to Management of Gestational Diabetes Mellitus (GDM) and Recommendations for Developing a Mobile App for GDM

ObjectivesMobile technology is increasingly used to augment management and treatment of gestational diabetes mellitus (GDM), however its use is limited in low and middle income countries like Nepal. We conducted a qualitative study in order to inform the development of a culturally-appropriate mobile app that supports management of GDM among patients in Dhulikhel Hospital, Nepal.MethodsA total of 12 women with GDM diagnosis (either current or in the preceding 1 year) were recruited from a tertiary level hospital in Nepal. In order to explore the perceived barriers and facilitators to GDM management, we conducted focus group (1 with 4 participants) and in-depth interviews (IDIs; n = 8) with GDM patients, as well as IDIs with their spouses (n = 2) and GDM care providers (n = 5). Towards the end of the focus group and IDIs, the prototype for the proposed GDM app was shown, and feedback was sought on app's features and function. Focus group and IDIs were transcribed verbatim and thematic analysis was undertaken using manual coding.ResultsWe identified several facilitators to GDM management including at the individual level (e.g., concern for baby's health), family level (e.g., accompaniment to hospital visits, emotional support) and health system level (e.g., universal GDM screening, team approach to management). Notable barriers included inadequate time for diet/lifestyle counseling during hospital visits, abrupt change in diet/lifestyle from pre- to post GDM diagnosis, misconceptions around diet and physical activity, as well as social/cultural barriers including food-centered traditions and festivities, and lack of decision-making power in the household. Majority of GDM patients and their spouses indicated that they lacked sufficient information to manage GDM and were frustrated by frequent hospital visits. All participants agreed that the proposed mobile app would be useful and relevant to women with GDM. They believed it would help overcome existing barriers by empowering pregnant women with information and tools to manage GDM and track their progress.ConclusionsOur user research affirmed the potential utility of our target app, and provided important insight into app features that would need to be incorporated to meet patient needs and knowledge gaps, as well as address the barriers related to GDM management.Funding SourcesNIH/FIC.

Applying a User-Centric Design Approach to Develop a Mobile Application for Management and Treatment of Gestational Diabetes in Nepal

ObjectivesMobile apps can aid with the management of Gestational Diabetes Mellitus (GDM) by providing patient education, and reinforcing daily glucose monitoring and diet/lifestyle modification. Here we describe our process of applying a user-centered design approach in developing a culturally-appropriate app (GDM-DH) to support the management of GDM among patients in Dhulikhel Hospital, Nepal.MethodsA multidisciplinary team including experts in GDM, mHealth, and behavior and implementation sciences, as well as health providers and patients at Dhulikhel Hospital, contributed to the development of the GDM-DH app. After finalizing the app's content and features, we created the app's wireframe, which illustrated the app's proposed interface, navigation sequences, and features and functions. This was followed by key informant interviews with healthcare providers (n = 5), focus groups, and in-depth interviews with GDM patients (n = 12) in which they were asked to provide feedback on the features and functions of the wireframe. Incorporating their input, we built the minimum viable product, which was user-tested with 6 GDM patients, and further refined for the final version of the GDM-DH app.ResultsQualitative findings from the user research affirmed the potential utility and usability of our target app and provided insight into app features and design elements that needed to be added or modified. Consistent with the Social Cognitive Theory, the final version of the GDM-DH app supports GDM self-management by providing health education and allowing patients to record and self-monitor blood glucose levels, carbohydrate intake, physical activity, and gestational weight gain. The app uses innovative features to minimize the self-monitoring burden and uses automatic feedback and data visualizations. Healthcare providers can use the web-based admin portal of the GDM-DH app to enter/review glucose readings, track patient progress, and guide treatment and counseling accordingly.ConclusionsTo our knowledge, this is the first mHealth platform for GDM developed for a low-income country, and the first one containing a feature to aid with carbohydrate estimation in meals. A pilot clinical trial is currently underway to explore the clinical utility of the GDM-DH app.Funding SourcesNIH/FIC.

Identifying candidate genes and drug targets for Alzheimer's disease by an integrative network approach using genetic and brain region-specific proteomic data.

Genome-wide association studies (GWAS) have identified more than 75 genetic variants associated with Alzheimer's disease (ad). However, how these variants function and impact protein expression in brain regions remain elusive. Large-scale proteomic datasets of ad postmortem brain tissues have become available recently. In this study, we used these datasets to investigate brain region-specific molecular pathways underlying ad pathogenesis and explore their potential drug targets. We applied our new network-based tool, Edge-Weighted Dense Module Search of GWAS (EW_dmGWAS), to integrate ad GWAS statistics of 472 868 individuals with proteomic profiles from two brain regions from two large-scale ad cohorts [parahippocampal gyrus (PHG), sample size n = 190; dorsolateral prefrontal cortex (DLPFC), n = 192]. The resulting network modules were evaluated using a scale-free network index, followed by a cross-region consistency evaluation. Our EW_dmGWAS analyses prioritized 52 top module genes (TMGs) specific in PHG and 58 TMGs in DLPFC, of which four genes (CLU, PICALM, PRRC2A and NDUFS3) overlapped. Those four genes were significantly associated with ad (GWAS gene-level false discovery rate < 0.05). To explore the impact of these genetic components on TMGs, we further examined their differentially co-expressed genes at the proteomic level and compared them with investigational drug targets. We pinpointed three potential drug target genes, APP, SNCA and VCAM1, specifically in PHG. Gene set enrichment analyses of TMGs in PHG and DLPFC revealed region-specific biological processes, tissue-cell type signatures and enriched drug signatures, suggesting potential region-specific drug repurposing targets for ad.

Optimal Packet Camouflage Against Traffic Analysis

Research has proved that supposedly secure encrypted network traffic is actually threatened by privacy and security violations from many aspects. This is mainly due to flow features leaking evidence about user activity and data content. Currently, adversaries can use statistical traffic analysis to create classifiers for network applications and infer users’ sensitive data. In this article, we propose a system that optimally prevents traffic feature leaks. In our first algorithm, we model the packet length probability distribution of the source app to be protected and that of the target app that the source app will resemble. We define a model that mutates the packet lengths of a source app to those lengths from the target app having similar bin probability. This would confuse a classifier by identifying a mutated source app as the target app. In our second obfuscation algorithm, we present an optimized scheme resulting in a trade-off between privacy and complexity overhead. For this reason, we propose a mathematical model for network obfuscation. We formulate analytically the problem of selecting the target app and the length from the target app to mutate to. Then, we propose an algorithm to solve it dynamically. Extensive evaluation of the proposed models, on real app traffic traces, shows significant obfuscation efficiency with relatively acceptable overhead. We were able to reduce a classification accuracy from 91.1% to 0.22% using the first algorithm, with 11.86% padding overhead. The same classification accuracy was reduced to 1.76% with only 0.73% overhead using the second algorithm.

Design and implementation of smart flying ball target based on APP

Home entertainment smart products have gradually become a hot topic. At present, home entertainment smart products are relatively simple to implement. They mainly use hardware or software methods to control and transmit information through Bluetooth or Zigbee. In order to solve the shortcomings of home entertainment intelligent products that lack intelligence and a single design method in the current market, this article uses embedded technology and Wi-Fi communication technology to design an intelligent flying ball target control system based on Android APP. Based on the hardware platform, the filtering algorithm and scoring algorithm of the flying target are studied. The flying target software system based on μC / GUI and μC / OS-III is written. The Android operating system is designed to connect to the flying ball target APP.

An Application-Specific Approach to Energy Optimization on Android Mobile Devices

On current Android mobile devices, DVFS is a key power management technique. Besides CPU, memory and GPU support DVFS as well. OS modules known as governors implement algorithms that dynamically adjust operating frequency for their respective hardware components to balance performance and power. However, the stock governors do not consider application characteristics, manager power instead of energy, and operate independently of each other, thus leading to energy inefficiency. In this paper, we present an application-specific approach to energy optimization targeting Android mobile devices. In this two-step approach, we first perform offline profiling of the target app to obtain its performance and power measurements under different DVFS settings. The profiling data are then used by a feedback loop based online controller to dynamically select energy-optimal DVFS settings for the app while trying to meet a performance target. In contrast with the default governors, our approach is application-specific, manages energy, and controls both CPU and memory in unison. Tested on a few mainstream applications, our solution achieves considerable energy savings with minimal performance loss. This paper describes the design and evaluation of the system in detail with a particular focus on problems related to the robustness of the approach.

Scalable Power Impact Prediction of Mobile Sensing Applications at Pre-Installation Time

Today's smartphone application (hereinafter ‘app’) markets do not provide information on power consumption of apps, which is essential for users. Continuous sensing apps make this problem more severe because significant power is consumed without the users’ awareness. We propose PowerForecaster to break through such an exhaustive cycle. It provides users with personalized estimation of sensing apps' power cost at pre-installation time. It is challenging to provide such estimation in advance because the actual power cost of a sensing app varies depending on user behavior such as physical activities and phone use patterns. To address this, we develop a novel power emulator as a core component of PowerForecaster. It achieves accurate, personalized power estimation by reproducing users' behaviors and emulating the target app's power use. We optimize the system to make the power emulation fast and its trace collection energy efficient. We further address the problem of dealing with large-scale emulation requests from worldwide deployment. We develop a novel selective emulation approach to minimize the server-side resource cost. We performed extensive experiments and the experimental results show that PowerForecaster achieves the power estimation accuracy of 93.4 percent and saves on 60 percent of the emulator instance usage.

EstiDroid: Estimate API Calls of Android Applications Using Static Analysis Technology

Tracking API calls of an Android application (app) has significant value for deeply understanding the app's running behaviors, so that to detect security damages, sensitive information leakages, energy consumptions, system resources occupations of the app, etc. However, existing methods track API calls of a target app through launching and manipulating the app in a real or simulated operating environment. The entire process is time consuming, which leads to low efficiency for practical system executing batch analysis for a considerable scale of apps. In order to enhance the speed of API calls tracking, in this paper, we propose a static analysis method, called EstiDroid, to estimate API calls of Android apps by statically analyzing the apps without actually running them. EstiDroid is composed of a static analyzer and an estimation algorithm. To analyze a target app, EstiDroid first obtains several types of static information from the app's.APK file via the static analyzer, then, the estimation algorithm is employed to establish the estimation model for the app based on the static information. Finally, according to the model, the proportion of each API's calls in the total number of calls is estimated. In experiments, 300 apps are tested via EstiDroid and manual operation in smartphone, the results show that EstiDroid only consumed 49242ms on average compared with manual testing, and it reached 84.06% average similarity and 90.74% maximum similarity compared with the API calls tracked in real environments.

An Entropy-Based Solution for Identifying Android Packers

By using wrapping techniques, malicious developers can build a packed app (or packer) to deceive the basic analysis layer and successfully publish it through Android`s application distribution service (such as Google Play) before being detected by an in-depth analysis process. Although a thorough dynamic analysis can help in identifying packers, it is impossible to analyze mass numbers of apps in a short time. Regarding static analysis, most of the existing research on identifying packers depends on the signature of packers such as files or directories. In this paper, we propose a model for detecting packers through static analysis of various contents in an Android Package file. By applying entropy-based algorithms in conjunction with common classification solutions, our model can provide output about whether a target app has the potential to be a packer or not through prediction values. Compared with previous solutions with absolute results, the relative results could increase the likelihood of identifying variants from known packers. The results of our experiment have shown that our model can identify potential packers with high accuracy. Our proposed model can help improve the detection of potential packers as well as contribute new research directions for detecting packer by static analysis.

Cocreated Smartphone App to Improve the Quality of Life of Adolescents and Young Adults with Cancer (Kræftværket): Protocol for a Quantitative and Qualitative Evaluation.

BackgroundAdolescents and young adults with cancer face significant challenges during the course of their medical treatment and recovery from illness. Many adolescents and young adults struggle with long-term complications in the physical, psychosocial, economic, and academic domains. Mobile health (mHealth) interventions provide an innovative platform for delivering supportive care, particularly through the utilization of apps on smartphones and tablets. To create a successful mHealth intervention for adolescents and young adults, youth input and feedback is essential. The process of cocreation, in which the target app user has a direct role in dictating design and function, was utilized to create the prototype smartphone app for adolescents and young adults with cancer, “Kræftværket.”ObjectiveThe objective of this paper is to describe the protocol for the evaluation of the Kræftværket app, a prototype app designed via cocreation, to support and improve health-related quality of life for adolescents and young adults with cancer.MethodsThe Kræftværket app has three primary features, (1) a symptom and activity diary, (2) a supportive communication network between app users, and (3) a “one-stop shop” information bank with practical information as well as links to patient organizations and other resources. The app will be evaluated in two phases, a pilot test and an implementation test. In the pilot test, the app will be launched to a test group of 20 adolescents and young adults aged 15 to 29 years, selected for equal representation amongst age group and treatment status. Patients will be allowed to utilize the app over the course of six weeks and will complete a baseline and follow-up European Organization for Research and Treatment of Cancer Quality of Life Questionnaire Core 30 (EORTC QLQ-C30) health-related quality of life inventory. In addition, participant focus group interviews will be conducted according to a semistructured interview guide. Resulting data will be analyzed using thematic analysis. Results and appropriate analysis from both the qualitative and quantitative branches of the pilot test will be discussed amongst the research group, and appropriate changes based on user feedback will be made to the app before the final project phase. In the implementation test, the app will be provided and utilized by a sample of 50 adolescents and young adults aged 15-29 years selected for equal representation amongst gender, age group, diagnosis, and treatment status over the course of 3 months. Participants will be asked to complete a baseline and follow-up EORTC QLQ-C30 HRQoL inventory.ResultsPilot testing is expected to take place in February 2018, and implementation testing is expected to begin May 2018.ConclusionsIt is the hope that Kræftværket app will serve as a beneficial and easily utilized product. The process of evaluating the app and its effect on quality of life will address the absence of evidence-based mHealth interventions, and attempt to validate new approaches to benefitting adolescents and young adult oncology patients in the digital world.Registered Report IdentifierRR1-10.2196/10098

DroidInjector: A process injection-based dynamic tracking system for runtime behaviors of Android applications

As the most widely applied mobile operating system for smartphones, Android is challenged by fast growing security problems, which are caused by malicious applications (apps). Behaviors of malicious apps become more and more inconspicuous, which largely increase the difficulty of security detection. This paper provides a new dynamic method, called DroidInjector, to further enrich Android malware detection technologies. DroidInjector is a process injection-based dynamic tracking method for monitoring the behaviors of target app during its running period. Distinguished with existing works, 1) DroidInjector uses a ptrace-based technology to attach itself to the process of the target app, so tracking can be done on smartphones or emulators, and without modifying Android OS; 2) DroidInjector can monitor security-sensitive Java API calls in Android Runtime (Android Virtual Machine) by hooking the APIs related to Android component lifecycle phases, dynamic library loading, multi-threading, inter-component and inter-process communications, and system resources. Thus, DroidInjector supports flexible deployments, and is able to provide a fine-grained context-aware, flow-aware and library-aware API calls tracking for the target app. DroidInjector is validated in extensive experiments through performance evaluation, application evaluation and case evaluation for multiple malicious and benign apps running on several smartphones installed with standard or customized Android systems.

Matching Mobile Applications for Cross Promotion

The mobile app market is one of the most successful software markets. As the platform grows rapidly, with millions of apps and billions of users, search costs are increasing tremendously. The challenge is how app developers can target the right users with their apps and how consumers can find apps that fit their needs. Cross promotion, advertising a mobile app (target app) in another app (source app), is introduced as a new app promotion framework to alleviate the issue of search costs. In this paper, we model source app user behaviors (downloads and post-download usage) with respect to different target apps in cross-promotion campaigns. We construct a novel app similarity measure using LDA topic modeling on apps’ production descriptions, and then analyze how the similarity between the source and target apps influences users’ app download and usage decisions. To estimate the model, we use a unique data set from a large-scale random matching experiment conducted by a major mobile advertising company in Korea. The empirical results show that consumers prefer more diversified apps when they are making download decisions compared with their usage decisions, which is supported by the psychology literature on people’s variety-seeking behavior. Lastly, we propose an app-matching system based on machine learning models (on app download and usage prediction) and generalized deferred acceptance algorithms. The simulation results show that app analytics capability is essential in building accurate prediction models and in increasing ad effectiveness of cross promotion campaigns, and that, at the expense of privacy, individual user data can further improve the matching performance. The paper has implications on the tradeoff between utility and privacy in the growing mobile economy.

안드로이드 앱 변조 방지를 위한 APK 덮어쓰기 기법

It is easy to reverse engineer an Android app package file(APK) and get its decompiled source code. Therefore, attackers obtains economic benefits by illegally using the decompiled source code, or modifies an app by inserting malware. To address these problems in Android, we propose an APK overwrite scheme that protects apps against illegal modification of themselves by using a new anti-reverse engineering technique. In this paper, the targets are the apps which have been written by any programmer. For a target app (original app), server system (1) makes a copy of a target app, (2) encrypts the target app, (3) creates a stub app by replacing the DEX (Dalvik Executable) of the copied version with our stub DEX, and then (4) distributes the stub app as well as the encrypted target app to users of smartphones. The users downloads both the encrypted target app and the corresponding stub app. Whenever the stub app is executed on smartphones, the stub app and our launcher app decrypt the encrypted target app, overwrite the stub app with the decrypted target one, and executes the decrypted one. Every time the target app ends its execution, the decrypted app is deleted. To verify the feasibility of the proposed scheme, experimentation with several popular apps are carried out. The results of the experiment demonstrate that our scheme is effective for preventing reverse engineering and tampering of Android apps. 논문 14-39B-05-08 The Journal of Korea Information and Communications Society '14-05 Vol.39B No.05 http://dx.doi.org/10.7840/kics.2014.39B.5.309 309 ※ 본 연구는 미래창조과학부가 지원한 2014년 정보통신·방송(ICT) 연구개발사업과 문화체육관광부 및 한국저작권위원회의 2014년도 저작권 기술개발사업의 연구결과로 수행되었습니다. First Author : Research Institute of Information and Communication Convergence Technology, Dankook University, notanything@dankook.ac.kr, 정회원 ° Corresponding Author : Dept. of Computer Science, Dankook University, sjcho@dankook.ac.kr, 정회원 * 단국대학교 컴퓨터학과, ggudwns@dankook.ac.kr, lchan12@dankook.ac.kr, anaislover@dankook.ac.kr 논문번호:KICS2014-04-118, Received April 7, 2014; Revised April 16, 2014; Accepted April 16, 2014 The Journal of Korea Information and Communications Society '14-05 Vol.39B No.05

PDroid: Detecting Privacy Leakage on Android

The prevalence of Android makes it face the severe security threats from malicious apps. Many Android malware can steal users’ sensitive data and leak them out. The data flow analysis is a popular technique used to detect privacy leakages by tracking the sensitive information flow statically. In practice, an effective data flow analysis should employ inter-procedure information tracking. However, the Android event-driven programming model brings a challenge to construct the call graph (CG) for a target app. This paper presents a method which employs the inter-procedural and context-sensitive data flow analysis to detect privacy leakage in Android apps. To make the analysis accurate, a flow-sensitive and points-to call target analysis is employed to construct and improve the call graph. A prototype system, called PDroid, has been implemented and applied to some real malware. The experiment shows that our method can effective detect the privacy leakages cross multiple method call instances.