In Split Federated Learning (SFL), the clients collaboratively train a model with the help of a server by splitting the model into two parts. Part-1 is trained locally at each client and aggregated by the aggregator at the end of each round. Part-2 is trained at a server that sequentially processes the intermediate activations received from each client. We study the phenomenon of catastrophic forgetting (CF) in SFL in the presence of data heterogeneity. In detail, due to the nature of SFL, local updates of part-1 may drift away from global optima, while part-2 is sensitive to the processing sequence, similar to forgetting in continual learning (CL). Specifically, we observe that the trained model performs better in classes (labels) seen at the end of the sequence. We investigate this phenomenon with emphasis on key aspects of SFL, such as the processing order at the server and the cut layer. Based on our findings, we propose Hydra, a novel mitigation method inspired by multi-head neural networks and adapted for the SFL setting. Extensive numerical evaluations show that Hydra outperforms baselines and methods from the literature.

Web pixels are one of the predominant techniques for tracking conversions and user behavior on the Web. The integration of Meta Pixel (the most widely used tracking pixel) into a website enables Meta to collect sensitive information about the website's visitors and match it with their Facebook or Instagram profiles. In addition to detailed navigation history, Meta Pixel also collects personally identifiable information (PII) entered by visitors in online forms present on the website, such as emails and phone numbers.

As Automatic Speech Recognition (ASR) models become ever more pervasive, it is important to ensure that they make reliable predictions under corruptions present in the physical and digital world. We propose Speech Robust Bench (SRB), a comprehensive benchmark for evaluating the robustness of ASR models to diverse corruptions. SRB is composed of 114 input perturbations which simulate an heterogeneous range of corruptions that ASR models may encounter when deployed in the wild. We use SRB to evaluate the robustness of several state-of-the-art ASR models and observe that model size and certain modeling choices such as the use of discrete representations, or self-training appear to be conducive to robustness. We extend this analysis to measure the robustness of ASR models on data from various demographic subgroups, namely English and Spanish speakers, and males and females. Our results revealed noticeable disparities in the model's robustness across subgroups. We believe that SRB will significantly facilitate future research towards robust ASR models, by making it easier to conduct comprehensive and comparable robustness evaluations.

Ensuring user privacy remains a critical concern within mobile cellular networks, particularly given the proliferation of interconnected devices and services. In fact, a lot of user privacy issues have been raised in 2G, 3G, and 4G networks. Acknowledging these concerns, 3GPP has prioritized addressing these issues in the development of 5G, implementing numerous modifications to enhance user privacy since 5G Release 15. In this systematization of knowledge paper, we first provide a framework for studying privacy and security related attacks in cellular networks, setting as privacy objective the User Identity Confidentiality defined in 3GPP standards. Using this framework, we discuss existing privacy and security attacks in pre-5G networks, analyzing the weaknesses that lead to these attacks. Furthermore, we thoroughly study the security characteristics of 5G up to the latest 5G-Advanced Release 19, and examine mitigation mechanisms of 5G to the identified pre-5G attacks. Afterwards, we analyze how recent 5G attacks try to overcome these mitigation mechanisms. Finally, we identify current limitations and open problems in security of 5G, and propose directions for future work.

The online advertising market has recently reached the 500 billion dollar mark. To accommodate the need to match a user with the highest bidder at a fraction of a second, it has moved towards a complex, automated and often opaque model that involves numerous agents and intermediaries. Stimulated by the lack of transparency, but also the enormous potential profits, bad actors have found ways to circumvent restrictions, and generate substantial revenue that can support websites with objectionable or even illegal content.

Over the past few years, the European Commission has made significant steps to reduce disinformation in cyberspace. One of those steps has been the introduction of the 2022 ''Strengthened Code of Practice on Disinformation''. Signed by leading online platforms, this Strengthened Code of Practice on Disinformation is an attempt to combat disinformation on the Web. The Code of Practice includes a variety of measures including the demonetization of disinformation, urging, for example, advertisers ''to avoid the placement of advertising next to Disinformation content''.

Recent work highlights how gradient-level access can lead to successful inference and reconstruction attacks against federated learning (FL). In such settings, differentially private (DP) learning is known to provide resilience. However, approaches used in the status quo (i.e., central and local DP) introduce disparate utility vs. privacy trade-offs. In this work, we mitigate such trade-offs through hierarchical FL (HFL). For the first time, we demonstrate that by the introduction of a new intermediary level where calibrated noise can be added, better trade-offs can be obtained; we term this hierarchical DP (HDP). Our experiments with 3 different datasets (commonly used as benchmarks for FL in prior works) suggest that HDP produces models as accurate as those obtained using central DP, where noise is added at a central aggregator at a lower privacy budget.

Training and deploying Machine Learning models that simultaneously adhere to principles of fairness and privacy while ensuring good utility poses a significant challenge. The interplay between these three factors of trustworthiness is frequently underestimated and remains insufficiently explored. Consequently, many efforts focus on ensuring only two of these factors, neglecting one in the process. The decentralization of the datasets and the variations in distributions among the clients exacerbate the complexity of achieving this ethical trade-off in the context of Federated Learning (FL). For the first time in FL literature, we address these three factors of trustworthiness. We introduce PUFFLE, a high-level parameterised approach that can help in the exploration of the balance between utility, privacy, and fairness in FL scenarios. We prove that PUFFLE can be effective across diverse datasets, models, and data distributions, reducing the model unfairness up to 75%, with a maximum reduction in the utility of 17% in the worst-case scenario, while maintaining strict privacy guarantees during the FL training.

Despite its enormous economical and societal impact, lack of human-perceived control and safety is re-defining the design and development of emerging AI-based technologies. New regulatory requirements mandate increased human control and oversight of AI, transforming the development practices and responsibilities of individuals interacting with AI. In this paper, we present the SPATIAL architecture, a system that augments modern applications with capabilities to gauge and monitor trustworthy properties of AI inference capabilities. To design SPATIAL, we first explore the evolution of modern system architectures and how AI components and pipelines are integrated. With this information, we then develop a proof-of- concept architecture that analyzes AI models in a human-in-the- loop manner. SPATIAL provides an AI dashboard for allowing individuals interacting with applications to obtain quantifiable insights about the AI decision process. This information is then used by human operators to comprehend possible issues that influence the performance of AI models and adjust or counter them. Through rigorous benchmarks and experiments in real- world industrial applications, we demonstrate that SPATIAL can easily augment modern applications with metrics to gauge and monitor trustworthiness, however, this in turn increases the complexity of developing and maintaining systems implementing AI. Our work highlights lessons learned and experiences from augmenting modern applications with mechanisms that support regulatory compliance of AI. In addition, we also present a road map of on-going challenges that require attention to achieve robust trustworthy analysis of AI and greater engagement of human oversight.

Ensuring user privacy remains critical in mobile networks, particularly with the rise of connected devices and denser 5G infrastructure. Privacy concerns have persisted across 2G, 3G, and 4G/LTE networks. Recognizing these concerns, the 3rd Generation Partnership Project (3GPP) has made privacy enhancements in 5G Release 15. However, the extent of operator adoption remains unclear, especially as most networks operate in 5G Non Stand Alone (NSA) mode, relying on 4G Core Networks. This study provides the first qualitative and experimental comparison between 5G NSA and Stand Alone in real operator networks, focusing on privacy enhancements addressing top 8 pre-5G attacks based on recent academic literature. Also, it evaluates the privacy levels of OpenAirInterface, a leading open-source software for 5G, against real network deployments for the same attacks, revealing two new 5G privacy vulnerabilities, and underscoring the need for further studies and stricter standards.

Automatic fake news detection is a challenging problem in misinformation spreading, and it has tremendous real-world political and social impacts. Past studies have proposed machine learning-based methods for detecting such fake news, focusing on different properties of the published news articles, such as linguistic characteristics of the actual content, which however have limitations due to the apparent language barriers. Departing from such efforts, we propose Fake News Detection-as-a-Service (FNDaaS), the first automatic, content-agnostic fake news detection method, that considers new and unstudied features such as network and structural characteristics per news website. This method can be enforced as-a-Service, either at the ISP-side for easier scalability and maintenance, or user-side for better end-user privacy. We demonstrate the efficacy of our method using more than 340K datapoints crawled from existing lists of 637 fake and 1183 real news websites, and by building and testing a proof of concept system that materializes our proposal. Our analysis of data collected from these websites shows that the vast majority of fake news domains are very young and appear to have lower time periods of an IP associated with their domain than real news ones. By conducting various experiments with machine learning classifiers, we demonstrate that FNDaaS can achieve an AUC score of up to 0.967 on past sites, and up to 77-92% accuracy on newly-flagged ones.

In the pursuit of bolstering user safety, social media platforms deploy active moderation strategies, including content removal and user suspension. These measures target users engaged in discussions marked by hate speech or toxicity, often linked to specific keywords or hashtags. Nonetheless, the increasing prevalence of toxicity indicates that certain users adeptly circumvent these measures.This study examines consistently toxic users on Twitter (rebranded as X) Rather than relying on traditional methods based on specific topics or hashtags, we employ a novel approach based on patterns of toxic tweets, yielding deeper insights into their behavior.We analyzed 38 million tweets from the timelines of 12,148 Twitter users and identified the top 1,457 users who consistently exhibit toxic behavior, relying on metrics like the Gini index and Toxicity score. By comparing their posting patterns to those of non-consistently toxic users, we have uncovered distinctive temporal patterns, including contiguous activity spans, inter-tweet intervals (referred to as “Burstiness”), and churn analysis. These findings provide strong evidence for the existence of a unique tweeting pattern associated with toxic behavior on Twitter.Crucially, our methodology transcends Twitter and can be adapted to various social media platforms, facilitating the identification of consistently toxic users based on their posting behavior. This research contributes to ongoing efforts to combat online toxicity and offers insights for refining moderation strategies in the digital realm. We are committed to open research and will provide our code and data to the research community.

The argument for persistent social media influence campaigns, often funded by malicious entities, is gaining traction. These entities utilize instrumented profiles to disseminate divisive content and disinformation, shaping public perception. Despite ample evidence of these instrumented profiles, few identification methods exist to locate them in the wild. To evade detection and appear genuine, small clusters of instrumented profiles engage in unrelated discussions, diverting attention from their true goals [34]. This strategic thematic diversity conceals their selective polarity towards certain topics and fosters public trust [49]. This study aims to characterize profiles potentially used for influence operations, termed “on-mission profiles,” relying solely on thematic content diversity within unlabeled data. Distinguishing this work is its focus on content volume and toxicity towards specific themes. Longitudinal data from 138K Twitter (rebranded as X) profiles and 293M tweets enables profiling based on theme diversity. High thematic diversity groups predominantly produce toxic content concerning specific themes, like politics, health, and news—classifying them as “on-mission” profiles. Using the identified on-mission” profiles, we design a classifier for unseen, unlabeled data. Employing a linear SVM model, we train and test it on an 80/20% split of the most diverse profiles. The classifier achieves a flawless 100% accuracy, facilitating the discovery of previously unknown “on-mission” profiles in the wild.

Contemporary browsers constitute a critical component of our everyday interactions with the Web. Similar to a small, but powerful operating system, a browser is responsible to fetch and run web apps locally, on the user's (mobile) device. Even though in the last few years, there has been an increased interest for tools and mechanisms to block potentially malicious behaviours of web domains against the users' privacy (e.g., ad blockers, incognito browsing mode, etc.), it is still unclear if the user can browse the Web in private.

Over the past years, advertisement companies have used various tracking methods to persistently track users across the web. Such tracking methods usually include first and third-party cookies, cookie synchronization, as well as a variety of fingerprinting mechanisms. Facebook (FB) (now Meta) recently introduced a new tagging mechanism that attaches a one-time tag as a URL parameter (namely FBCLID) on outgoing links to other websites. Although such a tag does not seem to have enough information to persistently track users, we demonstrate that despite its ephemeral nature, when combined with FB Pixel, it can aid in persistently monitoring user browsing behavior across i) different websites, ii) different actions on each website, iii) time, i.e., both in the past as well as in the future. We refer to this online monitoring of users as FB web tracking.

Fake news is an age-old phenomenon, widely assumed to be associated with political propaganda published to sway public opinion. Yet, with the growth of social media, it has become a lucrative business for Web publishers. Despite many studies performed and countermeasures proposed, unreliable news sites have increased in the last years their share of engagement among the top performing news sources. Stifling fake news impact depends on our efforts in limiting the (economic) incentives of fake news producers.

A machine learning model can often produce biased outputs for a familiar group or similar sets of classes during inference over an unknown dataset. The generalization of neural networks have been studied to resolve biases, which has also shown improvement in accuracy and performance metrics, such as precision and recall, and refining the dataset's validation set. Data distribution and instances included in test and validation-set play a significant role in improving the generalization of neural networks. For producing an unbiased AI model, it should not only be trained to achieve high accuracy and minimize false positives. The goal should be to prevent the dominance of one class/feature over the other class/feature while calculating weights. This paper investigates state-of-art object detection/classification on AI models using metrics such as selectivity score and cosine similarity. We focus on perception tasks for vehicular edge scenarios, which generally include collaborative tasks and model updates based on weights. The analysis is performed using cases that include the difference in data diversity, the viewpoint of the input class and combinations. Our results show the potential of using cosine similarity, selectivity score and invariance for measuring the training bias, which sheds light on developing unbiased AI models for future vehicular edge services.

Federated Learning (FL) [2] has emerged as a popular solution of Confidential Computing [3] to distributedly train a model on user devices, improving privacy and system scalability. Such privacy-preserving models can be used in wide range of applications, and especially in Telco networks [4]. However, there are no practical systems to easily enable FL training on mobile apps, and especially in an as-a-service fashion. In this demo, we implement and test FLaaS, our recently proposed end-to-end FL service [1]. FLaaS includes a client-side framework with app library and service, and a back-end server, to enable secure and easy to deploy intra- and inter-app FL model training on mobile environments.

Digital advertising is the most popular way for content monetization on the Internet. Publishers spawn new websites, and older ones change hands with the sole purpose of monetizing user traffic. In this ever-evolving ecosystem, it is challenging to effectively answer questions such as: Which entities monetize what websites? What categories of websites does an average entity typically monetize on and how diverse are these websites? How has this website administration ecosystem changed across time? In this paper, we propose a novel, graph-based methodology to detect administration of websites on the Web, by exploiting the ad-related publisher-specific IDs. We apply our methodology across the top 1 million websites and study the characteristics of the created graphs of website administration. Our findings show that approximately 90% of the websites are associated each with a single publisher, and that small publishers tend to manage less popular websites. We perform a historical analysis of up to 8 million websites, and find a new, constantly rising number of (intermediary) publishers that control and monetize traffic from hundreds of websites, seeking a share of the ad-market pie. We also observe that over time, websites tend to move from big to smaller administrators.

Aggression in online social networks has been studied mostly from the perspective of machine learning, which detects such behavior in a static context. However, the way aggression diffuses in the network has received little attention as it embeds modeling challenges. In fact, modeling how aggression propagates from one user to another is an important research topic, since it can enable effective aggression monitoring, especially in media platforms, which up to now apply simplistic user blocking techniques. In this article, we address aggression propagation modeling and minimization in Twitter, since it is a popular microblogging platform at which aggression had several onsets. We propose various methods building on two well-known diffusion models, Independent Cascade ( IC ) and Linear Threshold ( LT ), to study the aggression evolution in the social network. We experimentally investigate how well each method can model aggression propagation using real Twitter data, while varying parameters, such as seed users selection, graph edge weighting, users’ activation timing, and so on. It is found that the best performing strategies are the ones to select seed users with a degree-based approach, weigh user edges based on their social circles’ overlaps, and activate users according to their aggression levels. We further employ the best performing models to predict which ordinary real users could become aggressive (and vice versa) in the future, and achieve up to AUC = 0.89 in this prediction task. Finally, we investigate aggression minimization by launching competitive cascades to “inform” and “heal” aggressors. We show that IC and LT models can be used in aggression minimization, providing less intrusive alternatives to the blocking techniques currently employed by Twitter.

Aggression on social media has evolved into a major point of concern. However, recently proposed machine learning (ML) approaches to detect various types of aggressive behavior fall short, due to the fast and increasing pace of content generation as well as evolution of such behavior over time. This work introduces the first, practical, real-time framework for detecting aggression on Twitter via embracing the streaming ML paradigm. This method adapts its ML binary classifiers in an incremental fashion, while receiving new annotated examples, and achieves similar performance as batch-based ML models, with 82-93% accuracy, precision, and recall. Experimental analysis on real Twitter data reveals how this framework, implemented in Spark Streaming, easily scales to process millions of tweets in minutes.

We propose and implement a Privacy-preserving Federated Learning ( PPFL ) framework for mobile systems to limit privacy leakages in federated learning. Leveraging the widespread presence of Trusted Execution Environments (TEEs) in high-end and mobile devices, we utilize TEEs on clients for local training, and on servers for secure aggregation, so that model/gradient updates are hidden from adversaries. Challenged by the limited memory size of current TEEs, we leverage greedy layer-wise training to train each model's layer inside the trusted area until its convergence. The performance evaluation of our implementation shows that PPFL can significantly improve privacy while incurring small system overheads at the client-side. In particular, PPFL can successfully defend the trained model against data reconstruction, property inference, and membership inference attacks. Furthermore, it can achieve comparable model utility with fewer communication rounds (0.54×) and a similar amount of network traffic (1.002×) compared to the standard federated learning of a complete model. This is achieved while only introducing up to ~15% CPU time, ~18% memory usage, and ~21% energy consumption overhead in PPFL's client-side.

During the past few years, mostly as a result of the GDPR and the CCPA, websites have started to present users with cookie consent banners. These banners are web forms where the users can state their preference and declare which cookies they would like to accept, if such option exists. Although requesting consent before storing any identifiable information is a good start towards respecting the user privacy, yet previous research has shown that websites do not always respect user choices. Furthermore, considering the ever decreasing reliance of trackers on cookies and actions browser vendors take by blocking or restricting third-party cookies, we anticipate a world where stateless tracking emerges, either because trackers or websites do not use cookies, or because users simply refuse to accept any.

The rise of online aggression on social media is evolving into a major point of concern. Several machine and deep learning approaches have been proposed recently for detecting various types of aggressive behavior. However, social media are fast paced, generating an increasing amount of content, while aggressive behavior evolves over time. In this work, we introduce the first, practical, real-time framework for detecting aggression on Twitter via embracing the streaming machine learning paradigm. Our method adapts its ML classifiers in an incremental fashion as it receives new annotated examples and is able to achieve the same (or even higher) performance as batch-based ML models, with over 90% accuracy, precision, and recall. At the same time, our experimental analysis on real Twitter data reveals how our framework can easily scale to accommodate the entire Twitter Firehose (of 778 million tweets per day) with only 3 commodity machines. Finally, we show that our framework is general enough to detect other related behaviors such as sarcasm, racism, and sexism in real time.

Attention networks constitute the state-of-the-art paradigm for capturing long temporal dynamics. This paper examines the efficacy of this paradigm in the challenging task of emotion recognition in dyadic conversations. In this work, we introduce a novel attention mechanism capable of inferring the immensity of the effect of each past utterance on the current speaker emotional state. The proposed self-attention network captures the correlation patterns among consecutive encoder network states, thus enabling the robust and effective modeling of temporal dynamics over arbitrary long temporal horizons. We exhibit the effectiveness of our approach considering the challenging IEMOCAP benchmark. We show that, our devised methodology outperforms state-of-the-art alternatives and commonly used approaches, giving rise to promising new research directions in the context of Online Social Network (OSN) analysis tasks.

Websites with hyper-partisan, left or right-leaning focus offer content that is typically biased towards the expectations of their target audience. Such content often polarizes users, who are repeatedly primed to specific (extreme) content, usually reflecting hard party lines on political and socio-economic topics. Though this polarization has been extensively studied with respect to content, it is still unknown how it associates with the online tracking experienced by browsing users, especially when they exhibit certain demographic characteristics. For example, it is unclear how such websites enable the ad-ecosystem to track users based on their gender or age.

Being able to check whether an online advertisement has been targeted is essential for resolving privacy controversies and implementing in practice data protection regulations like GDPR, CCPA, and COPPA. In this paper we describe the design, implementation, and deployment of an advertisement auditing system called eyeWnder that uses crowdsourcing to reveal in real time whether a display advertisement has been targeted or not. Crowdsourcing simplifies the detection of targeted advertising, but requires reporting to a central repository the impressions seen by different users, thereby jeopardizing their privacy. We break this deadlock with a privacy preserving data sharing protocol that allows eyeWnder to compute global statistics required to detect targeting, while keeping the advertisements seen by individual users and their browsing history private. We conduct a simulation study to explore the effect of different parameters and a live validation to demonstrate the accuracy of our approach. Unlike previous solutions, eyeWnder can even detect indirect targeting, i.e. , marketing campaigns that promote a product or service whose description bears no semantic overlap with its targeted audience.

In recent years, Header Bidding (HB) has gained popularity among web publishers, challenging the status quo in the ad ecosystem. Contrary to the traditional waterfall standard, HB aims to give back to publishers control of their ad inventory, increase transparency, fairness and competition among advertisers, resulting in higher ad-slot prices. Although promising, little is known about how this ad protocol works: What are HB's possible implementations, who are the major players, and what is its network and UX overhead?

The proliferation of Big Data applications puts pressure on improving and optimizing the handling of diverse datasets across different domains. Among several challenges, major difficulties arise in data-sensitive domains like banking, telecommunications, etc., where strict regulations make very difficult to upload and experiment with real data on external cloud resources. In addition, most Big Data research and development efforts aim to address the needs of IT experts, while Big Data analytics tools remain unavailable to non-expert users to a large extent. In this paper, we report on the work-in-progress carried out in the context of the H2020 project I-BiDaaS (Industrial-Driven Big Data as a Self-service Solution) which aims to address the above challenges. The project will design and develop a novel architecture stack that can be easily configured and adjusted to address cross-sectoral needs, helping to resolve data privacy barriers in sensitive domains, and at the same time being usable by non-experts. This paper discusses and motivates the need for Big Data as a self-service, reviews the relevant literature, and identifies gaps with respect to the challenges described above. We then present the I-BiDaaS paradigm for Big Data as a self-service, position it in the context of existing references, and report on initial work towards the conceptual specification of the I-BiDaaS software architecture.

User data is the primary input of digital advertising, fueling the free Internet as we know it. As a result, web companies invest a lot in elaborate tracking mechanisms to acquire user data that can sell to data markets and advertisers. However, with same-origin policy and cookies as a primary identification mechanism on the web, each tracker knows the same user with a different ID. To mitigate this, Cookie Synchronization (CSync) came to the rescue, facilitating an information sharing channel between 3rd-parties that may or not have direct access to the website the user visits. In the background, with CSync, they merge user data they own, but also reconstruct a user's browsing history, bypassing the same origin policy. In this paper, we perform a first to our knowledge in-depth study of CSync in the wild, using a year-long weblog from 850 real mobile users. Through our study, we aim to understand the characteristics of the CSync protocol and the impact it has on web users' privacy. For this, we design and implement CONRAD, a holistic mechanism to detect CSync events at real time, and the privacy loss on the user side, even when the synced IDs are obfuscated. Using CONRAD, we find that 97% of the regular web users are exposed to CSync: most of them within the first week of their browsing, and the median userID gets leaked, on average, to 3.5 different domains. Finally, we see that CSync increases the number of domains that track the user by a factor of 6.75.

Video sharing platforms like YouTube are increasingly targeted by aggression and hate attacks. Prior work has shown how these attacks often take place as a result of "raids," i.e., organized efforts by ad-hoc mobs coordinating from third-party communities. Despite the increasing relevance of this phenomenon, however, online services often lack effective countermeasures to mitigate it. Unlike well-studied problems like spam and phishing, coordinated aggressive behavior both targets and is perpetrated by humans, making defense mechanisms that look for automated activity unsuitable. Therefore, the de-facto solution is to reactively rely on user reports and human moderation. In this paper, we propose an automated solution to identify YouTube videos that are likely to be targeted by coordinated harassers from fringe communities like 4chan. First, we characterize and model YouTube videos along several axes (metadata, audio transcripts, thumbnails) based on a ground truth dataset of videos that were targeted by raids. Then, we use an ensemble of classifiers to determine the likelihood that a video will be raided with very good results (AUC up to 94%). Overall, our work provides an important first step towards deploying proactive systems to detect and mitigate coordinated hate attacks on platforms like YouTube.

Cyberbullying and cyberaggression are increasingly worrisome phenomena affecting people across all demographics. More than half of young social media users worldwide have been exposed to such prolonged and/or coordinated digital harassment. Victims can experience a wide range of emotions, with negative consequences such as embarrassment, depression, isolation from other community members, which embed the risk to lead to even more critical consequences, such as suicide attempts. In this work, we take the first concrete steps to understand the characteristics of abusive behavior in Twitter, one of today’s largest social media platforms. We analyze 1.2 million users and 2.1 million tweets, comparing users participating in discussions around seemingly normal topics like the NBA, to those more likely to be hate-related, such as the Gamergate controversy, or the gender pay inequality at the BBC station. We also explore specific manifestations of abusive behavior, i.e., cyberbullying and cyberaggression, in one of the hate-related communities (Gamergate). We present a robust methodology to distinguish bullies and aggressors from normal Twitter users by considering text, user, and network-based attributes. Using various state-of-the-art machine-learning algorithms, we classify these accounts with over 90% accuracy and AUC. Finally, we discuss the current status of Twitter user accounts marked as abusive by our methodology and study the performance of potential mechanisms that can be used by Twitter to suspend users in the future.

Botnets in online social networks are increasingly often affecting the regular flow of discussion, attacking regular users and their posts, spamming them with irrelevant or offensive content, and even manipulating the popularity of messages and accounts. Researchers and cybercriminals are involved in an arms race, and new and updated botnets designed to defeat current detection systems are constantly developed, rendering such detection systems obsolete.

With current technology, a number of entities have access to user mobility traces at different levels of spatio-temporal granularity. At the same time, users frequently reveal their location through different means, including geo-tagged social media posts and mobile app usage. Such leaks are often bound to a pseudonym or a fake identity in an attempt to preserve one's privacy. In this work, we investigate how large-scale mobility traces can de-anonymize anonymous location leaks. By mining the country-wide mobility traces of tens of millions of users, we aim to understand how many location leaks are required to uniquely match a trace, how spatio-temporal obfuscation decreases the matching quality, and how the location popularity and time of the leak influence de-anonymization. We also study the mobility characteristics of those individuals whose anonymous leaks are more prone to identification. Finally, by extending our matching methodology to full traces, we show how large-scale human mobility is highly unique. Our quantitative results have implications for the privacy of users' traces, and may serve as a guideline for future policies regarding the management and publication of mobility data.

Digital advertisements are delivered in the form of static images, animations or videos, with the goal to promote a product, a service or an idea to desktop or mobile users. Thus, the advertiser pays a monetary cost to buy ad-space in a content provider»s medium (e.g., website) to place their advertisement in the consumer»s display. However, is it only the advertiser who pays for the ad delivery? Unlike traditional advertisements in mediums such as newspapers, TV or radio, in the digital world, the end-users are also paying a cost for the advertisement delivery. Whilst the cost on the advertiser»s side is clearly monetary, on the end-user, it includes both quantifiable costs, such as network requests and transferred bytes, and qualitative costs such as privacy loss to the ad ecosystem. In this study, we aim to increase user awareness regarding the hidden costs of digital advertisement in mobile devices, and compare the user and advertiser views. Specifically, we built OpenDAMP, a transparency tool that passively analyzes users» web traffic and estimates the costs in both sides. We use a year-long dataset of 1270 real mobile users and by juxtaposing the costs of both sides, we identify a clear imbalance: the advertisers pay several times less to deliver ads, than the cost paid by the users to download them. In addition, the majority of users experience a significant privacy loss, through the personalized ad delivery mechanics.

While there is an increasing flow of media stories reporting cases of cyberbullying, particularly within online social media, research efforts in the academic community are scattered over different topics across the social science and computer science academic disciplines. In this work, we explored research pertaining to cyberbullying, conducted across disciplines. We mainly sought to understand scholarly activity on intelligence techniques for the detection of cyberbullying when it occurs. Our findings suggest that the vast majority of academic contributions on cyberbullying focus on understanding the phenomenon, risk factors, and threats, with the prospect of suggesting possible protection strategies. There is less work on intelligence techniques for the detection of cyberbullying when it occurs, while currently deployed algorithms seem to detect the problem only up to some degree of success. The article summarises the current trends aiming to encourage discussion and research with a new scope; we call for more research tackling the problem by leveraging statistical models and computational mechanisms geared to detect, intervene, and prevent cyberbullying. Coupling intelligence techniques with specific web technology problems can help combat this social menace. We argue that a multidisciplinary approach is needed, with expertise from human–computer interaction, psychology, computer science, and sociology, for current challenges to be addressed and significant progress to be made.

We study the problem of extracting a selective connector for a given set of query vertices Q subset of V in a graph G = (V,E). A selective connector is a subgraph of G which exhibits some cohesiveness property, and contains the query vertices but does not necessarily connect them all. Relaxing the connectedness requirement allows the connector to detect multiple communities and to be tolerant to outliers. We achieve this by introducing the new measure of network inefficiency and by instantiating our search for a selective connector as the problem of finding the minimum inefficiency subgraph.

Online advertising is progressively moving towards a programmatic model in which ads are matched to actual interests of individuals collected as they browse the web. Letting the huge debate around privacy aside, a very important question in this area, for which little is known, is: How much do advertisers pay to reach an individual?

As the number and the diversity of news outlets on the Web grows, so does the opportunity for "alternative" sources of information to emerge. Using large social networks like Twitter and Facebook, misleading, false, or agenda-driven information can quickly and seamlessly spread online, deceiving people or influencing their opinions. Also, the increased engagement of tightly knit communities, such as Reddit and 4chan, further compounds the problem, as their users initiate and propagate alternative information, not only within their own communities, but also to different ones as well as various social media. In fact, these platforms have become an important piece of the modern information ecosystem, which, thus far, has not been studied as a whole.

IoT big data requires new machine learning methods able to scale to large size of data arriving at high speed. Decision trees are popular machine learning models since they are very effective, yet easy to interpret and visualize. In the literature, we can find distributed algorithms for learning decision trees, and also streaming algorithms, but not algorithms that combine both features. In this paper we present the Vertical Hoeffding Tree (VHT), the first distributed streaming algorithm for learning decision trees. It features a novel way of distributing decision trees via vertical parallelism. The algorithm is implemented on top of Apache SAMOA, a platform for mining big data streams, and thus able to run on real-world clusters. Our experiments to study the accuracy and throughput of VHT prove its ability to scale while attaining superior performance compared to sequential decision trees.

Betweenness centrality measures the importance of an element of a graph, either a vertex or an edge, by the fraction of shortest paths that pass through it [1]. This measure is notoriously expensive to compute, and the best known algorithm, proposed by Brandes [2], runs in O(nm) time. The problems of efficiency and scalability are exacerbated in a dynamic setting, where the input is an evolving graph seen edge by edge, and the goal is to keep the betweenness centrality up to date. In this paper [8] we propose the first truly scalable and practical framework for computing vertex and edge betweenness centrality of large evolving graphs, incrementally and online.

Carefully balancing load in distributed stream processing systems has a fundamental impact on execution latency and throughput. Load balancing is challenging because real-world workloads are skewed: some tuples in the stream are associated to keys which are significantly more frequent than others. Skew is remarkably more problematic in large deployments: having more workers implies fewer keys per worker, so it becomes harder to “average out” the cost of hot keys with cold keys. We propose a novel load balancing technique that uses a heavy hitter algorithm to efficiently identify the hottest keys in the stream. These hot keys are assigned to d ≥ 2 choices to ensure a balanced load, where d is tuned automatically to minimize the memory and computation cost of operator replication. The technique works online and does not require the use of routing tables. Our extensive evaluation shows that our technique can balance real-world workloads on large deployments, and improve throughput and latency by 150% and 60% respectively over the previous state-of-the-art when deployed on Apache Storm.

Community-based question answering (CQA) platforms are crowd-sourced services for sharing user expertise on various topics, from mechanical repairs to parenting. While they naturally build-in an online social network infrastructure, they carry a very different purpose from Facebook-like social networks, where users "hang-out" with their friends and tend to share more personal information. It is unclear, thus, how the privacy concerns and their correlation with user behavior in an online social network translate into a CQA platform. This study analyzes one year of recorded traces from a mature CQA platform to understand the association between users' privacy concerns as manifested by their account settings and their activity in the platform. The results show that privacy preference is correlated with behavior in the community in terms of engagement, retention, accomplishments and deviance from the norm. We find privacy-concerned users have higher qualitative and quantitative contributions, show higher retention, report more abuses, have higher perception on answer quality and have larger social circles. However, at the same time, these users also exhibit more deviant behavior than the users with public profiles.

We study the problem of load balancing in distributed stream processing engines, which is exacerbated in the presence of skew. We introduce Partial Key Grouping (PKG), a new stream partitioning scheme that adapts the classical “power of two choices” to a distributed streaming setting by leveraging two novel techniques: key splitting and local load estimation. In so doing, it achieves better load balancing than key grouping while being more scalable than shuffle grouping. We test PKG on several large datasets, both real-world and synthetic. Compared to standard hashing, PKG reduces the load imbalance by up to several orders of magnitude, and often achieves nearly-perfect load balance. This result translates into an improvement of up to 60% in throughput and up to 45% in latency when deployed on a real Storm cluster.

Advanced and effective collaborative filtering methods based on explicit feedback assume that unknown ratings do not follow the same model as the observed ones not missing at random). In this work, we build on this assumption, and introduce a novel dynamic matrix factorization framework that allows to set an explicit prior on unknown values. When new ratings, users, or items enter the system, we can update the factorization in time independent of the size of data (number of users, items and ratings). Hence, we can quickly recommend items even to very recent users. We test our methods on three large datasets, including two very sparse ones, in static and dynamic conditions. In each case, we outrank state-of-the-art matrix factorization methods that do not use a prior on unknown ratings.

Many decentralized online social networks (DOSNs) have been proposed due to an increase in awareness related to privacy and scalability issues in centralized social networks. Such decentralized networks transfer processing and storage functionalities from the service providers towards the end users. DOSNs require individualistic implementation for services, (i.e., search, information dissemination, storage, and publish/subscribe). However, many of these services mostly perform social queries, where OSN users are interested in accessing information of their friends. In our work, we design a socially-aware distributed hash table (DHTs) for efficient implementation of DOSNs. In particular, we propose a gossip-based algorithm to place users in a DHT, while maximizing the social awareness among them. Through a set of experiments, we show that our approach reduces the lookup latency by almost 30% and improves the reliability of the communication by nearly 10% via trusted contacts.

The Wiener index of a graph is the sum of all pairwise shortest-path distances between its vertices. In this paper we study the novel problem of finding a minimum Wiener connector: given a connected graph G=(V,E) and a set Q ⊆ V of query vertices, find a subgraph of G that connects all query vertices and has minimum Wiener index.

Community-based question answering platforms can be rich sources of information on a variety of specialized topics, from finance to cooking. The usefulness of such platforms depends heavily on user contributions (questions and answers), but also on respecting the community rules. As a crowd-sourced service, such platforms rely on their users for monitoring and flagging content that violates community rules. Common wisdom is to eliminate the users who receive many flags. Our analysis of a year of traces from a mature Q&A site shows that the number of flags does not tell the full story: on one hand, users with many flags may still contribute positively to the community. On the other hand, users who never get flagged are found to violate community rules and get their accounts suspended. This analysis, however, also shows that abusive users are betrayed by their network properties: we find strong evidence of homophilous behavior and use this finding to detect abusive users who go under the community radar. Based on our empirical observations, we build a classifier that is able to detect abusive users with an accuracy as high as 83%.

Betweenness centrality is a classic measure that quantifies the importance of a graph element (vertex or edge) according to the fraction of shortest paths passing through it. This measure is notoriously expensive to compute, and the best known algorithm runs in O(nm) time. The problems of efficiency and scalability are exacerbated in a dynamic setting, where the input is an evolving graph seen edge by edge, and the goal is to keep the betweenness centrality up to date. In this paper, we propose the first truly scalable algorithm for online computation of betweenness centrality of both vertices and edges in an evolving graph where new edges are added and existing edges are removed. Our algorithm is carefully engineered with out-of-core techniques and tailored for modern parallel stream processing engines that run on clusters of shared-nothing commodity hardware. Hence, it is amenable to real-world deployment. We experiment on graphs that are two orders of magnitude larger than previous studies. Our method is able to keep the betweenness centrality measures up-to-date online, i.e., the time to update the measures is smaller than the inter-arrival time between two consecutive updates.

An unprecedented information wealth produced by online social networks, further augmented by location/collocation data, is currently fragmented across different proprietary services. Combined, it can accurately represent the social world and enable novel socially aware applications. We present Prometheus, a socially aware peer-to-peer service that collects social information from multiple sources into a multigraph managed in a decentralized fashion on user-contributed nodes, and exposes it through an interface implementing nontrivial social inferences while complying with user-defined access policies. Simulations and experiments on PlanetLab with emulated application workloads show the system exhibits good end-to-end response time, low communication overhead, and resilience to malicious attacks.

Web traffic represents a powerful mirror for various real-world phenomena. For example, it was shown that web search volumes have a positive correlation with stock trading volumes and with the sentiment of investors. Our hypothesis is that user browsing behavior on a domain-specific portal is a better predictor of user intent than web searches.

Friend-to-Friend (F2F) storage systems were shown to suffer from two significant limitations. First, users with a small set of friends are penalized by lack of available storage for their needs, while users with many friends get overloaded with resource requests. Second, friends are typically in close geographical proximity to each other, and thus their online times are synchronized, leading to low data availability when they are offline. This paper addresses these concerns by expanding the set of storage resources while still using a measure of social incentives. It proposes an indirect tie measurement to compute the social strength between possibly distant nodes in a social network. Using datasets from co-authorship networks and a video gaming community, we show that the social strength-based mechanism more than doubles the set of storage candidates motivated by social incentives, invites socially low connected users to contribute more resources and improves data availability by up to 6.5 times. We also show that our method complemented with simple load balancing strategies significantly improves peer engagement and workload distribution in F2F storage systems.

Online gaming is a multi-billion dollar industry that entertains a large, global population. One unfortunate phenomenon, however, poisons the competition and spoils the fun: cheating. The costs of cheating span from industry-supported expenditures to detect and limit it, to victims’ monetary losses due to cyber crime. This article studies cheaters in the Steam Community, an online social network built on top of the world’s dominant digital game delivery platform. We collected information about more than 12 million gamers connected in a global social network, of which more than 700 thousand have their profiles flagged as cheaters. We also observed timing information of the cheater flags, as well as the dynamics of the cheaters’ social neighborhoods. We discovered that cheaters are well embedded in the social and interaction networks: their network position is largely indistinguishable from that of fair players. Moreover, we noticed that the number of cheaters is not correlated with the geographical, real-world population density, or with the local popularity of the Steam Community. Also, we observed a social penalty involved with being labeled as a cheater: cheaters lose friends immediately after the cheating label is publicly applied. Most importantly, we observed that cheating behavior spreads through a social mechanism: the number of cheater friends of a fair player is correlated with the likelihood of her becoming a cheater in the future. This allows us to propose ideas for limiting cheating contagion.

Social applications mine user social graphs to improve performance in search, provide recommendations, allow resource sharing and increase data privacy. When such applications are implemented on a peer-to-peer (P2P) architecture, the social graph is distributed on the P2P system: the traversal of the social graph translates into a socially-informed routing in the peer-to-peer layer. In this work we introduce the model of a projection graph that is the result of decentralizing a social graph onto a peer-to-peer network. We focus on three social network metrics: degree, node betweenness and edge betweenness centrality and analytically formulate the relation between metrics in the social graph and in the projection graph. We demonstrate with an application scenario on real social networks the usability of the projection graph properties in designing social search applications and unstructured P2P overlays that exhibit improved performance and reduced overhead over baseline scenarios.

Online gaming is a multi-billion dollar industry that entertains a large, global population. One unfortunate phenomenon, however, poisons the competition and the fun: cheating. The costs of cheating span from industry-supported expenditures to detect and limit cheating, to victims' monetary losses due to cyber crime. This paper studies cheaters in the Steam Community, an online social network built on top of the world's dominant digital game delivery platform. We collected information about more than 12 million gamers connected in a global social network, of which more than 700 thousand have their profiles flagged as cheaters. We also collected in-game interaction data of over 10 thousand players from a popular multiplayer gaming server. We show that cheaters are well embedded in the social and interaction networks: their network position is largely indistinguishable from that of fair players. We observe that the cheating behavior appears to spread through a social mechanism: the presence and the number of cheater friends of a fair player is correlated with the likelihood of her becoming a cheater in the future. Also, we observe that there is a social penalty involved with being labeled as a cheater: cheaters are likely to switch to more restrictive privacy settings once they are tagged and they lose more friends than fair players. Finally, we observe that the number of cheaters is not correlated with the geographical, real-world population density, or with the local popularity of the Steam Community.

Social applications implemented on a peer-to-peer (P2P) architecture mine the social graph of their users for improved performance in search, recommendations, resource sharing and others. In such applications, the social graph that connects their users is distributed on the peer-to-peer system: the traversal of the social graph translates to a socially-informed routing in the peer-to-peer layer. In this work we introduce the model of a projection graph that is the result of mapping a social graph onto a peer-to-peer network. We analytically formulate the relation between metrics in the social graph and in the projection graph. We focus on three such graph metrics: degree centrality, node betweenness centrality, and edge betweenness centrality. We evaluate experimentally the feasibility of estimating these metrics in the projection graph from the metrics of the social graph. Our experiments on real networks show that when mapping communities of 50-150 users on a peer, there is an optimal organization of the projection graph with respect to degree and node betweenness centrality. In this range, the association between the properties of the social graph and the projection graph is the highest, and thus the properties of the (dynamic) projection graph can be inferred from the properties of the (slower changing) social graph. We discuss the applicability of our findings to aspects of peer-to-peer systems such as data dissemination, social search, peer vulnerability, and data placement and caching.