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Characterizing, Modeling and Exploiting the Mobile Demand Footprint of Large Public Protests
No full textSmartphones and mobile applications are staple tools in the operation of current-age public demonstrations, where they support organizers and participants in, e.g., scaling the management of the events or communicating live about their objectives and traction. The widespread use of mobile services during protests also presents interesting opportunities to observe the dynamics of these manifestations from a digital perspective. Previous studies in that direction have focused on the analysis of content posted in selected social media so as to forecast, survey or ascertain the success of public protests. In this paper, we take a different viewpoint and present a holistic characterization of the consumption of the whole spectrum of mobile applications during social protests. Hinging upon pervasive measurements in the production network of the incumbent network operator and focusing on the 2023 French pension reform strikes, we unveil how large masses of protesters generate a clearly recognizable footprint on mobile service demands in the examined events. In fact, the footprint is so strong that it lets us develop models informed by the usage of selected mobile applications that are capable of (i) tracking the spatiotemporal evolution of the target demonstrations and (ii) estimate the time-varying number of attendees from aggregate network operator data only. We demonstrate the utility of such privacy-preserving models to perform a-posteriori analyses of the public protests that reveal, e.g., the precise progression of the marches, alternate minor routes taken by participants or their dispersal at the end of the events.TRUEinpres
A Study of Malicious Source Code Reuse Among GitHub, StackOverflow and Underground Forums
Get PDFTo date, most analysis of collaboration between malware authors has been performed on meta-data and compiled binaries, while ignoring artifacts present in the source code. We collect a vast amount of malicious source code from Underground Forums posts, Underground Forum code attachments, and GitHub repositories and devise a methodology that allows us to filter out most auxiliary code, leaving the measurement to focus on malicious code. We leverage this to perform an in-depth measurement of the reuse of malicious code between these malware centers as well as StackOverflow. We find that our methodology has high precision in identifying malicious code (93.1%) and provides a contemporary snapshot of malware code reuse across the Web, offering insights into the manners in which this takes place.Spanish Ministry of Science and InnovationEuropean Union-NextGenerationERDFTRUEinpres
Online advertisement in a pink-colored market
Get PDFIt is surprising that women are often charged more for products and services marketed explicitly to them. This phenomenon, known as the pink tax, is a major issue that questions women’s buying power. Nevertheless, it is not just limited to physical products - even online advertising can be subject to this type of gender-price discrimination. That is where our research comes in. We have developed a new methodology to measure what we call the digital marketing pink tax - the additional expense of delivering advertisements to female audiences.
Analyzing data from Facebook advertising platforms across 187 countries and 40 territories shows this issue is systematic. Particularly, the digital marketing pink tax is prevalent in 79% of audiences across the world and 98% of audiences in highly developed countries. Therefore, advertisers incur a median cost of 30% more to display advertisements to women than men. In contrast, advertisers have to pay less digital marketing pink tax in less-developed countries (5%). Our research indicates that countries in the Middle East and Africa with a low Human Development Index (HDI) do not experience this phenomenon. Our comprehensive investigation of 24 industries reveals that advertisers must pay up to 64% of the digital marketing pink tax to target women in some industries. Our findings also suggest a connection between the digital marketing pink tax and the consumer pink tax - the extra charge placed on products marketed to women. Overall, our research sheds light on an important issue affecting women worldwide. Raising awareness of the digital marketing pink tax and advocating for better regulation.TRUEpu
Sensing in Bi-Static ISAC Systems with Clock Asynchronism: A Signal Processing Perspective
Get PDFIntegrated Sensing and Communications (ISAC) has been identified as a pillar usage scenario for the impending 6G era. Bi-static sensing, a major type of sensing in \ac{isac}, is promising to expedite ISAC in the near future, as it requires minimal changes to the existing network infrastructure. However, a critical challenge for bi-static sensing is clock asynchronism due to the use of different clocks at far separated transmitter and receiver. This causes the received signal to be affected by time-varying random phase offsets, severely degrading, or even failing, direct sensing. Considerable research attention has been directed toward addressing the clock asynchronism issue in bi-static sensing. In this white paper, we endeavor to fill the gap by providing an overview of the issue and existing techniques developed in an ISAC background. Based on the review and comparison, we also draw insights into the future research directions and open problems, aiming to nurture the maturation of bi-static sensing in ISAC.TRUEpu
PriPrune: Quantifying and Preserving Privacy in Pruned Federated Learning
Get PDFModel pruning has been proposed as a technique for reducing the size and complexity of Federated learning (FL) models. By making local models coarser, pruning is intuitively expected to improve protection against privacy attacks. However, the level of this expected privacy protection has not been previously characterized, or optimized jointly with utility.
In this paper, we first characterize the privacy offered by pruning. We establish information-theoretic upper bounds on the information leakage from pruned FL and we experimentally validate them under state-of-the-art privacy attacks across different FL pruning schemes. Second, we introduce PriPrune – a privacy-aware algorithm for pruning in FL. PriPrune uses defense pruning masks, which can be applied locally after any pruning algorithm, and adapts the defense pruning rate to jointly optimize privacy and accuracy. Another key idea in the design of PriPrune is Pseudo-Pruning: it undergoes defense pruning within the local model and only sends the pruned model to the server; while the weights pruned out by defense mask are withheld locally for future local training rather than being removed. We show that PriPrune significantly improves the privacy-accuracy tradeoff compared to state-of-the-art pruned FL schemes. For example, on the FEMNIST dataset, PriPrune improves the privacy of PruneFL by 45.5% without reducing accuracy.Ministry of Economic Affairs and Digital Transformation, European Union NextGeneration-EU REGAGE22e00052829516TRUEpu
A Many-Objective Optimization Approach for Weight Gain and Animal Welfare in Rotational Grazing of Cattle
Get PDFThe “multidimensional” nature of the concept of welfare is reflected in the definition proposed by the World Organization for Animal Health (OIE), according to which an animal is in a satisfactory state of welfare when it is healthy, comfortable, and well-fed, can express its innate behavior, and does not suffer pain, fear, or distress. Many of these aspects, in the real context of a cattle farm, are not considered, and most of In this proposal, we establish a many-objective optimization model for rotational grazing allocation based on six objectives that consider cattle weight gain and travel, as well as their welfare. The model is solved using the NSGA-III algorithm, and its performance is evaluated using a simulation study of 90 days of rotational grazing in which it is compared with the traditional grazing strategy. Average weight gains of up to 36.7 kg per animal are achieved at the end of the three months of simulated grazing using the proposed model. The results indicate that the allocation model generates an average weight gain that is statistically greater than that generated by the traditional rotation method but also guarantees improved animal welfare, the main contribution of our approach.TRUEpu
FreqyWM: Frequency WaterMarking for the New Data Economy
Get PDFWe present a novel technique for modulating the appearance frequency of a few tokens within a dataset for encoding an invisible watermark that can be used to protect ownership rights upon data. We develop optimal as well as fast heuristic algorithms for creating and verifying such watermarks. We also demonstrate the robustness of our technique against various attacks and derive analytical bounds for the false positive probability of erroneously “detecting” a watermark on a dataset that does not carry it. Our technique is applicable to both single dimensional and multidimensional datasets, is independent of token type, and can be used in a variety of use cases that involve buying and selling data in contemporary data marketplaces.European Union’s HORIZONThe Ministry of Economic Affairs and Digital Transformation and the European Union-NextGenerationEU/PRTR.TRUEinpres
Cooperative Spectrum Sensing for Beyond-5G Networks in Fading Environments
Get PDFThe advent of pervasive wireless systems faces several challenges due to the massive data traffic growth resulting from the interconnection of billions of new devices. This makes it essential to provide smart decision-making in identifying available spectrum resources by sensing the radio frequency environment. In this study, we aim to improve the spectrum sensing process and enhance the detection efficiency of secondary users (sensing devices) in identifying primary users (transmitting devices). We consider a scenario in which secondary users are affected by noise and fading, and employ distributed detection and data fusion to combine data from geographically distributed sensors. The results show that collaborative spectrum sensing, where multiple SUs share their sensing data, significantly enhances detection performance. By applying optimization techniques to assign optimal weight vectors to the sensors, we further increase the detection performance of the primary user, where each one is affected by different noise factors. The study reveals that detection performance improves as more users collaborate, and this improvement is validated through scenarios with varying SNR values.TRUEpu
Dissecting Advanced Time Series Forecasting Models with AIChronoLens
Get PDFMobile traffic forecasting is instrumental in efficiently managing network resources. In this poster paper, we dissect the behavior of advanced time series forecasting techniques, namely DLinear and PatchTST, when applied to the problems of predicting future mobile traffic volumes. Being black-box models hard to interpret, we ground our analysis on EXplainable Artificial Intelligence (XAI) by using AIChronoLens, a new tool that links legacy XAI explanations with the temporal properties of the input sequences. We find that the DLinear significantly improves the prediction accuracy over PatchTST and state-of-the-art techniques like Long-Short Term Memory (LSTM). The analysis with AIChronoLens shows that, unlike PatchTST, DLinear is capable of focusing its prediction decisions on a few key samples of the input sequences, which makes it possible for DLinear to match the ground truth closely.TRUEinpres
Towards 6G: Architectural Innovations and Challenges in the ORIGAMI Framework
Get PDFAs research in mobile networks is already transitioning from 5G to 6G, we identify a set of fundamental barriers in the current 5G architecture that limit efficient and global operations. We propose innovative architectural solutions that can remove such barriers and lay the foundation for 6G systems. Specifically, we introduce three novel architectural components: the Global Service-Based Architecture (GSBA), the Compute Continuum Layer (CCL), and the Zero-Trust Layer (ZTL). These components collectively aim to enhance network efficiency, security, and scalability, addressing future mobile networks’ dynamic and demanding needs. Furthermore, we discuss the integration of Network Intelligence (NI) that exploits the afore- mentioned architectural innovations to ensure global operations and services. Ultimately, our proposed vision entails a more adaptive, secure, and intelligent network architecture, setting the groundwork for the next generation of mobile networksORIGAMI project has received funding from the Smart Networks and Services Joint Undertaking (SNS JU) under the European Union’s Horizon Europe research and innovation programme under Grant Agreement No. 101139270.L.E. Chatzieleftheriou is a Juan de la Cierva awardee (JDC2022- 050266-I), funded by MCIU/AEI/10.13039/501100011033 and the European Union “NextGenerationEU”/PRTR, and MADQuantum-CM project, funded by the Regional Government of Madrid and the EU “NextGenerationEU”/PRTR.TRUEpu