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1915 research outputs found
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Third best student paper award “Study on privacy of parental control applications”
Get PDFParental control applications are one kind of mobile software programs used by parents to monitor and control the
use that their kids make of their cellphone. In this study we aim to learn about the privacy of these applications. We combine manual analysis of the Android marketplace with dynamic analysis of the application itself to learn about the ecosystem of these apps and their privacy problems. We studied 17 different applications of which 10 where fully analyzed using dynamic analysis. We found 11 privacy issues in 8 of these 10 apps. We also found that 17% of the permissions requested by these apps are not clearly mapped to any app functionality and that 50% of the applications do not clearly explain their communication model to users.TRUEpu
Open Video Datasets over Operational Mobile Networks with MONROE
No full text(Open Datasets and Software Track)Video is becoming a killer application for mobile users. DASH and WebRTC are the brightest stars in this context. To assess the performance of DASH and WebRTC, we use a large number of programmable network probes spread over several countries in the context of the MONROE project. The probes allow to analyze the end-user performance in a repeatable and controllable way over operational mobile networks. We have generated a large dataset with more than 300 video streaming experiments so far, and growing. Our dataset consists of traces from video streams, performance
indicators captured during the streaming, and experiment metadata. The dataset captures the wide variability of video quality and unveils how broadband mobile access is still not offering consistent quality guarantees across several monitored countries and networks, especially for users on the move.We make both software and datasets (video data and metadata) publicly open.TRUEpu
Beyond Google Play: A Large-Scale Comparative Study of Chinese Android App Markets
Get PDFhttps://doi.org/10.1145/3278532.3278558China is one of the largest Android markets in the world. As Chinese users cannot access Google Play to buy and install Android apps, a number of independent app stores have emerged and compete in the Chinese app market. Some of the Chinese app stores are pre-installed vendor-specific app markets (e.g., Huawei, Xiaomi and OPPO), whereas others are maintained by large tech companies (e.g., Baidu, Qihoo 360 and Tencent). The nature of these app stores and the content available through them vary greatly, including their
trustworthiness and security guarantees.
As of today, the research community has not studied the Chinese Android ecosystem in depth. To fill this gap, we present the first large-scale comparative study that covers more than 6 million Android apps downloaded from 16 Chinese app markets and Google Play. We focus our study on catalog similarity across app stores, their features, publishing dynamics, and the prevalence of various forms of misbehavior (including the presence of fake, cloned and malicious apps). Our findings also suggest heterogeneous developer behavior across app stores, in terms of code maintenance, use of third-party services, and so forth. Overall, Chinese app markets perform substantially worse when taking active measures to protect mobile users and legit developers from deceptive and abusive actors, showing a significantly higher prevalence of malware, fake, and cloned apps than Google Play.TRUEpu
A Large-Scale Analysis of Facebook’s User-Base and User Engagement Growth
Get PDFUnderstanding the evolution of the user base as well as the user engagement of online services is critical not only for the service operators but also for customers, investors, and users. While we can find research works addressing this issue in online services, such as Twitter, MySpace, or Google+, such detailed analysis is missing for Facebook, which is currently the largest online social network. This paper presents the first detailed study on the demographic and geographic composition and evolution of the user base and user engagement in Facebook over a period of three years. To this end, we have implemented a measurement methodology that leverages the marketing API of Facebook to retrieve actual information about the number of total users and the number of daily active users across 230 countries and age groups ranging between 13 and 65+. The conducted analysis reveals that Facebook is still growing and geographically expanding. Moreover, the growth pattern is heterogeneous across age groups, genders, and geographical regions. In particular, from a demography perspective, Facebook shows the lowest growth pattern among adolescents. Gender-based analysis showed that growth among men is still higher than the growth in women. Our geographical analysis reveals that while Facebook growth is slower in western countries, it has the fastest growth in the developing countries mainly located in Africa and Central Asia; analyzing the penetration of these countries also shows that these countries are at earlier stages of Facebook penetration. Leveraging external socioeconomic datasets, we also showed that this heterogeneous growth can be characterized by indicators, such as availability and access to Internet, Facebook popularity, and factors related with population growth and gender inequality.pu
“Won’t Somebody Think of the Children?” Examining COPPA Compliance at Scale
Get PDFWe present a scalable dynamic analysis frame- work that allows for the automatic evaluation of the privacy behaviors of Android apps. We use our system to analyze mobile apps’ compliance with the Children’s Online Privacy Protection Act (COPPA), one of the few stringent privacy laws in the U.S. Based on our auto- mated analysis of 5,855 of the most popular free children’s apps, we found that a majority are potentially in violation of COPPA, mainly due to their use of third- party SDKs. While many of these SDKs offer configuration options to respect COPPA by disabling tracking and behavioral advertising, our data suggest that a majority of apps either do not make use of these options or incorrectly propagate them across mediation SDKs. Worse, we observed that 19% of children’s apps collect identifiers or other personally identifiable information (PII) via SDKs whose terms of service outright prohibit their use in child-directed apps. Finally, we show that efforts by Google to limit tracking through the use of a resettable advertising ID have had little success: of the 3,454 apps that share the resettable ID with advertisers, 66% transmit other, non-resettable, persistent identifiers as well, negating any intended privacy-preserving properties of the advertising ID.TRUEpu
Data-driven Evaluation of Anticipatory Networking in LTE Networks
Get PDFAnticipatory networking is a recent branch of network optimization based on prediction of the system state. Our work specifically tackles prediction-driven resource allocation for mobile networks. While some anticipatory networking concepts have been proposed in the literature, understanding of the potential real world gains is so far very limited. Future mobile networks will likely integrate such mechanisms, and thus it is of paramount importance to understand the actual performance improvements and in which scenarios they can be realized. Analyzing a month of LTE control channel information collected in four locations, we show how anticipatory networking can enhance current LTE networks. First, we propose a comprehensive optimization framework encompassing different forecasting solutions. Then, we provide a thorough analysis of the aggregated network traffic and the contributions of individual users. In particular, we show that predictable traffic accounts for more than 95% of the total traffic volume and that simple prediction and optimization techniques allow network operators to save 50% of the resources and/or on average more than double the offered data rate in our data set.pu
New Alternatives to Optimize Policy Classifiers
Get PDFGrowing expressiveness of services increases the size
of a manageable state at the network data plane. A service policy is an ordered set of classification patterns (classes) with actions; the same class can appear in multiple policies. Previous studies mostly concentrated on efficient representations of a single policy instance. In this work, we study space efficiency of multiple
policies, cutting down a classifier size by sharing instances of classes between policies that contain them. In this paper we identify conditions for such sharing, propose efficient algorithms and analyze them analytically. The proposed representations can
be deployed transparently on existing packet processing engines. Our results are supported by extensive evaluations.TRUEpu
Measurement Errors in R
Get PDFThis paper presents an R package to handle and represent measurements with errors in a very simple way. We briefly introduce the main concepts of metrology and propagation of uncertainty, and discuss related R packages. Building upon this, we introduce the errors package, which provides a class for associating uncertainty metadata, automated propagation and reporting. Working with errors enables transparent, lightweight, less error-prone handling and convenient representation of measurements with errors. Finally, we discuss the advantages, limitations and future work of computing with errors.pu
An Empirical Analysis of the Commercial VPN Ecosystem
Get PDFhttps://doi.org/10.1145/3278532.3278570Global Internet users increasingly rely on virtual private network (VPN) services to preserve their privacy, circumvent censorship, and access geo-filtered content. Due to their own lack of technical sophistication and the opaque nature of VPN clients, however, the vast majority of users have limited means to verify a given VPN service’s claims along any of these dimensions. We design an active measurement system to test various infrastructural and privacy aspects
of VPN services and evaluate 62 commercial providers. Our
results suggest that while commercial VPN services seem, on the whole, less likely to intercept or tamper with user traffic than other, previously studied forms of traffic proxying, many VPNs do leak user traffic—perhaps inadvertently—through a variety of means. We also find that a non-trivial fraction of VPN providers transparently proxy traffic, and many misrepresent the physical location of their vantage points: 5–30% of the vantage points, associated with 10% of the providers we study, appear to be hosted on servers located in countries other than those advertised to users.TRUEpu
openLEON: An End-to-End Emulator from the Edge Data Center to the Mobile Users
Get PDFTo support next generation services, 5G mobile network architectures are increasingly adopting emerging technlogies like software-defined networking (SDN) and network function virtualization (NFV). Core and radio access functionalities are virtualized and executed in edge data centers, in accordance with the Multi-Access Edge Computing (MEC) principle. While testbeds are an essential research tool for experimental evaluation in such environments, the landscape of data center and mobile network testbeds is fragmented. In this work, we aim at filling this gap by presenting penLEON, an open source muLti-access Edge cOmputiNg end-to-end emulator that operates from the edge data center to the mobile users. openLEON bridges the functionalities of existing emulators for data centers and mobile networks, i.e., Mininet and srsLTE, and makes it possible to evaluate and validate research ideas on all the components of an end-to-end mobile edge architecture.TRUEpu