IMDEA Networks Institute Digital Repository
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1915 research outputs found
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Tales from the Porn: A Comprehensive Privacy Analysis of the Web Porn Ecosystem
Get PDFModern privacy regulations, including the General Data Protection Regulation (GDPR) in the European Union, aim to control user tracking activities in websites and mobile applications. These privacy rules typically contain specific provisions and strict requirements for websites that provide sensitive material to end users such as sexual, religious, and health services. However, little is known about the privacy risks that users face when visiting such websites, and about their regulatory compliance. In this paper, we present the first comprehensive and large-scale analysis of 6,843 pornographic websites. We provide an exhaustive behavioral analysis of the use of tracking methods by these websites, and their lack of regulatory compliance, including the absence of age-verification mechanisms and methods to obtain informed user consent. The results indicate that, as in the regular web, tracking is prevalent across pornographic sites: 72% of the websites use third-party cookies and 5% leverage advanced user fingerprinting technologies. Yet, our analysis reveals a third-party tracking ecosystem semi-decoupled from the regular web in which various analytics and advertising services track users across, and outside, pornographic websites. We complete the paper with a regulatory compliance analysis in the context of the EU GDPR, and newer legal requirements to implement verifiable access control mechanisms (e.g., UK's Digital Economy Act). We find that only 16% of the analyzed websites have an accessible privacy policy and only 4% provide a cookie consent banner. The use of verifiable access control mechanisms is limited to prominent pornographic websites.TRUEpu
An NFV-Based Energy Scheduling Algorithm for a 5G Enabled Fleet of Programmable Unmanned Aerial Vehicles
Get PDFThe fifth generation of mobile networks (5G) is expected to provide diverse and stringent improvements such as greater connectivity, bandwidth, throughput, availability, improved coverage, and lower latency. Considering this, drones or Unmanned Aerial Vehicles (UAVs) and Internet of Things (IoT) devices are perfect examples of existing technology that can take advantage of the capabilities provided by 5G technology. In particular, UAVs are expected to be an important component of 5G networks implementations and support different communication requirements and applications. UAVs working together with 5G can potentially facilitate the deployment of standalone or complementary communications infrastructures, and, due to its rapid deployment, these solutions are suitable candidates to provide network services in emergency scenarios, natural disasters, and search and rescue missions. An important consideration in the deployment of a programmable drone fleet is to guarantee the reliability and performance of the services through consistent monitoring, control, and management scheme. In this regard, the Network Functions Virtualization (NFV) paradigm, a key technology within the 5G ecosystem, can be used to perform automation, management, and orchestration tasks. In addition, to ensure the coordination and reliability in the communications systems, considering that the UAVs have a finite lifetime and that eventually they must be replaced, a scheduling scheme is needed to guarantee the availability of services and efficient resource utilization. To this end, in this paper is presented an UAV scheduling scheme which leverages the potential offered by NFV. The proposed strategy, based on a brute-force search combinatorial algorithm, allows obtaining the optimal scheduling of UAVs in time, in order to efficiently deploy network services. Simulation results validate the performance of the proposed strategy, by providing the number of drones needed to meet certain levels of service availability. Furthermore, the strategy allows knowing the sequence of replacement of UAVs to ensure the optimal resource utilization.pu
Slicing Cell Resources: The Case of HTC and MTC Coexistence
Get PDFIn this paper we investigate the allocation of resources to slices on the radio interface of one cell. In particular, we develop a detailed stochastic model of the behaviour of the sliced cell radio access, including most features of the standard access procedures. Our model allows the computation of the throughput achieved by each slice, as well as the distribution of delays for each slice. The availability of a model capable of accurately predicting the performance achieved by services using different slices as a function of the cell parameters is extremely important for the automated run time management of the cell and for the correct setting of its parameters. Specifically, while our model can cope with a number of slices, we focus on the case of one cell comprising one slice for human type communications and one slice for machine type
communications, and we discuss relevant emerging behaviours in the slices performance, as functions of the cell parameters. We validate the analytical predictions by comparison against
the estimates of a detailed simulator, proving the accuracy of the model. Our model turns out to be very effective in providing insight and guidelines for allocation and management of resources
in cells hosting slices carrying traffic derived from services with different characteristics and performance requirements.TRUEpu
Optimizing mmWave Spatial Reuse: Signal-to-Interference Aware Beamtraining
Get PDFBoth IEEE 802.15.3c and 802.11ad use the 60 GHz band for high datarate Wireless Personal/Local Area Network applications. These millimeter-wave communications use very directional antennas since the small wavelength allows to integrate many small antenna elements to form a beamforming antenna array, enabling very high spatial reuse as can be found in dense indoor and IoT settings. However, earlier work shows that current mmWave systems are not as directional as theory would suggest, with significant interference that may prevent spatial reuse. In this work, we propose a centralized system that allows the network to carry out the beamtraining process not only to maximize signal power, but also taking into account other stations in order to minimize interference. This system is designed to work with unmodified clients. We implement and validate our system on commercial off-the-shelf 60 GHz hardware, achieving an average throughput gain of 24.67% for TCP traffic, and up to a twofold throughput gain in specific cases.TRUEpu
On The Ridiculousness of Notice and Consent: Contradictions in App Privacy Policies
Get PDFThe dominant privacy framework of the information age relies on notions of “notice and consent.” That is, service providers will disclose, often through privacy policies, their data collection practices, and users can then consent to their terms. However, it is unlikely that most users comprehend these disclosures, which is due in no small part to ambiguous, deceptive, and misleading statements. By comparing actual collection and sharing practices to disclosures in privacy policies, we demonstrate the scope of the problem.
Through analysis of 68,051 apps from the Google Play Store, their corresponding privacy policies, and observed data transmissions, we investigated the potential misrepresentations of apps in the Designed For Families (DFF) program, inconsistencies
in disclosures regarding third-party data sharing, as well as contradictory disclosures about secure data transmissions. We find that of the 8,030 DFF apps (i.e., apps directed at children), 9.1% claim that their apps are not directed at children, while 30.6% claim to have no knowledge that the received data comes from children. In addition, we observe that 10.5% of 68,051 apps share personal identifiers with third-party service providers, yet do not declare any in their privacy policies, and only 22.2% of the apps explicitly name third parties. This ultimately makes it not only difficult, but in most cases impossible, for users to establish where their personal data is being processed. Furthermore, we find that 9,424 apps do not use TLS when transmitting personal identifiers, yet 28.4% of these apps claim to take measures to secure data transfer. Ultimately, these divergences between disclosures and actual app behaviors illustrate the ridiculousness of the notice and consent framework.TRUEpu
Highlights of SIGCOMM 2018
No full textThe Association for Computing Machinery’s Special Interest Group on Data Communication (ACM SIGCOMM) is a leading venue for research in architectures, protocols, technologies, and applications of computer networking and data communication. The ACM SIGCOMM annual conference spans all aspects of networks and networked systems, including packet processing, hardware and software, virtualization, routing, mobility, sensors, energy consumption, novel applications of artificial intelligence to networking, and usability of underlying communication technologies. Its participants include distinguished academics, brilliant students, and representatives of major multinational companies. Centered on its highly selective main conference, SIGCOMM published many landmark works in networking and communications. The conference also involves workshops, tutorials, presentation of demos, posters, industrial demos, student research competition, topic previews, and mentoring sessions. SIGCOMM 2018 met in Budapest on August 20-25. The first visit of ACM SIGCOMM to Eastern Europe became an unqualified success. Encompassing a variety of traditional and newly introduced events, the six-day conference program attracted around 750 attendees overall, with about 700 people registered for the 3-day main conference. This talk reviews the technical main-conference program, highlighting interesting ideas in its papers and identifying emerging research trends.FALSEpu
50 Ways to Leak Your Data: An Exploration of Apps' Circumvention of the Android Permissions Systems
Get PDFModern smartphone platforms implement permission-based models to protect access to sensitive data and system resources. However, apps can circumvent the permission model and gain access to protected data without user consent by using both covert and side channels. Side channels present in the implementation of the permission system allow apps to access protected data and system resources without permission; whereas covert channels enable communication between two colluding apps so that one app can share its permission-protected data with another app lacking those permissions. Both pose threats to user privacy.
In this work, we make use of our infrastructure that runs hundreds of thousands of apps in an instrumented environment. This testing environment includes mechanisms to monitor apps' runtime behaviour and network traffic. We look for evidence of side and covert channels being used in practice by searching for sensitive data being sent over the network for which the sending app did not have permissions to access it. We then reverse engineer the apps and third-party libraries responsible for this behaviour to determine how the unauthorized access occurred. We also use software fingerprinting methods to measure the static prevalence of the technique that we discover among other apps in our corpus.
Using this testing environment and method, we uncovered a number of side and covert channels in active use by hundreds of popular apps and third-party SDKs to obtain unauthorized access to both unique identifiers as well as geolocation data. We have responsibly disclosed our findings to Google and have received a bug bounty for our work.TRUEpu
Improvements to the Massive Unsupervised Outlier Detection (MUOD) Algorithm
Get PDFWe present improvements to the Massive Unsupervised Outlier Detection (MUOD) algorithm, a scalable and unsupervised outlier detection method, especially useful for identifying outliers for functional data. MUOD identifies different types of outliers in samples of curves including shape, magnitude and amplitude outliers. This is done by computing for each curve three indices, which measure outlyingness in terms of shape, magnitude and amplitude relative to the other curves. These indices are then sorted and observations with extremely high indices are labelled as outliers. To further improve the scalability MUOD, we introduce ``fastMUOD", a fast implementation of MUOD which uses the component-wise or the median in the computation of the indices instead of using the whole observation. We also present ``semi-fastMUOD", which uses a sample of the observations in the computation of the indices. As further improvements to MUOD, we discuss a new method for identifying extreme indices which entails the use of a classical boxplot or its adjusted version for skewed distributions. We analyse the performance of the proposed improvements using real and simulated data, and show that outlier detection accuracy is not compromised even with the gains in scalability.TRUEpu
Nameles: An intelligent system for Real-Time Filtering of Invalid Ad Traffic
Get PDFInvalid ad traffic is an inherent problem of programmatic advertising that has not been properly addressed so far. Traditionally, it has been considered that invalid ad traffic only harms the interests of advertisers, which pay for the cost of invalid ad impressions while other industry stakeholders earn revenue through commissions regardless of the quality of the impression. Our first contribution consists of providing evidence that shows how the Demand Side Platforms (DSPs), one of the most important intermediaries in the programmatic advertising supply chain, may be suffering from economic losses due to invalid ad traffic. Addressing the problem of invalid traffic at DSPs requires a highly scalable solution that can identify invalid traffic in real time at the individual bid request level. The second and main contribution is the design and implementation of a solution for the invalid traffic problem, a system that can be seamlessly integrated into the current programmatic ecosystem by the DSPs. Our system has been released under an open source license, becoming the first auditable solution for invalid ad traffic detection. The intrinsic transparency of our solution along with the good results obtained in industrial trials have led the World Federation of Advertisers to endorse it.TRUEpu