arXiv subjects

Costas Iordanou

Publications and source records attributed to Costas Iordanou.

10 recordsLinked to original sources

arXiv2024

Fundamental dynamics of popularity-similarity trajectories in real networks

Real networks are complex dynamical systems, evolving over time with the addition and deletion of nodes and links. Currently, there exists no principled mathematical theory for their dynamics -- a grand-challenge open problem. Here, we show that the popularity and similarity trajectories of nodes in hyperbolic embeddings of different real networks manifest universal self-similar properties with typical Hurst exponents $H \ll 0.5$. This means that the trajectories are predictable, displaying anti-persistent or 'mean-reverting' behavior, and they can be adequately captured by a fractional Brownian motion process. The observed behavior can be qualitatively reproduced in synthetic networks that possess a latent geometric space, but not in networks that lack such space, suggesting that the observed subdiffusive dynamics are inherently linked to the hidden geometry of real networks. These results set the foundations for rigorous mathematical machinery for describing and predicting real network dynamics.

physics.soc-ph↗

arXiv2022

Topology and Geometry of the Third-Party Domains Ecosystem: Measurement and Applications

Over the years, web content has evolved from simple text and static images hosted on a single server to a complex, interactive and multimedia-rich content hosted on different servers. As a result, a modern website during its loading time fetches content not only from its owner's domain but also from a range of third-party domains providing additional functionalities and services. Here, we infer the network of the third-party domains by observing the domains' interactions within users' browsers from all over the globe. We find that this network possesses structural properties commonly found in complex networks, such as power-law degree distribution, strong clustering, and small-world property. These properties imply that a hyperbolic geometry underlies the ecosystem's topology. We use statistical inference methods to find the domains' coordinates in this geometry, which abstract how popular and similar the domains are. The hyperbolic map we obtain is meaningful, revealing the large-scale organization of the ecosystem. Furthermore, we show that it possesses predictive power, providing us the likelihood that third-party domains are co-hosted; belong to the same legal entity; or merge under the same entity in the future in terms of company acquisition. We also find that complementarity instead of similarity is the dominant force driving future domains' merging. These results provide a new perspective on understanding the ecosystem's organization and performing related inferences and predictions.

cs.SI↗

arXiv2022

Securing Federated Sensitive Topic Classification against Poisoning Attacks

We present a Federated Learning (FL) based solution for building a distributed classifier capable of detecting URLs containing GDPR-sensitive content related to categories such as health, sexual preference, political beliefs, etc. Although such a classifier addresses the limitations of previous offline/centralised classifiers,it is still vulnerable to poisoning attacks from malicious users that may attempt to reduce the accuracy for benign users by disseminating faulty model updates. To guard against this, we develop a robust aggregation scheme based on subjective logic and residual-based attack detection. Employing a combination of theoretical analysis, trace-driven simulation, as well as experimental validation with a prototype and real users, we show that our classifier can detect sensitive content with high accuracy, learn new labels fast, and remain robust in view of poisoning attacks from malicious users, as well as imperfect input from non-malicious ones.

cs.CR↗

arXiv2021

What Is the Price of Data? A Measurement Study of Commercial Data Marketplaces

A large number of Data Marketplaces (DMs) have appeared in the last few years to help owners monetise their data, and data buyers fuel their marketing process, train their ML models, and perform other data-driven decision processes. In this paper, we present a first of its kind measurement study of the growing DM ecosystem and shed light on several totally unknown facts about it. For example, we show that the median price of live data products sold under a subscription model is around US\$1,400 per month. For one-off purchases of static data, the median price is around US\$2,200. We analyse the prices of different categories of data and show that products about telecommunications, manufacturing, automotive, and gaming command the highest prices. We also develop classifiers for comparing prices across different DMs as well as a regression analysis for revealing features that correlate with data product prices.

cs.CY↗

arXiv2021

A Unified Graph-Based Approach to Disinformation Detection using Contextual and Semantic Relations

As recent events have demonstrated, disinformation spread through social networks can have dire political, economic and social consequences. Detecting disinformation must inevitably rely on the structure of the network, on users particularities and on event occurrence patterns. We present a graph data structure, which we denote as a meta-graph, that combines underlying users' relational event information, as well as semantic and topical modeling. We detail the construction of an example meta-graph using Twitter data covering the 2016 US election campaign and then compare the detection of disinformation at cascade level, using well-known graph neural network algorithms, to the same algorithms applied on the meta-graph nodes. The comparison shows a consistent 3%-4% improvement in accuracy when using the meta-graph, over all considered algorithms, compared to basic cascade classification, and a further 1% increase when topic modeling and sentiment analysis are considered. We carry out the same experiment on two other datasets, HealthRelease and HealthStory, part of the FakeHealth dataset repository, with consistent results. Finally, we discuss further advantages of our approach, such as the ability to augment the graph structure using external data sources, the ease with which multiple meta-graphs can be combined as well as a comparison of our method to other graph-based disinformation detection frameworks.

cs.SI↗

arXiv2021

Did State-sponsored Trolls Shape the 2016 US Presidential Election Discourse? Quantifying Influence on Twitter

It is a widely accepted fact that state-sponsored Twitter accounts operated during the 2016 US presidential election, spreading millions of tweets with misinformation and inflammatory political content. Whether these social media campaigns of the so-called "troll" accounts were able to manipulate public opinion is still in question. Here, we quantify the influence of troll accounts on Twitter by analyzing 152.5 million tweets (by 9.9 million users) from that period. The data contain original tweets from 822 troll accounts identified as such by Twitter itself. We construct and analyse a very large interaction graph of 9.3 million nodes and 169.9 million edges using graph analysis techniques, along with a game-theoretic centrality measure. Then, we quantify the influence of all Twitter accounts on the overall information exchange as is defined by the retweet cascades. We provide a global influence ranking of all Twitter accounts and we find that one troll account appears in the top-100 and four in the top-1000. This combined with other findings presented in this paper constitute evidence that the driving force of virality and influence in the network came from regular users - users who have not been classified as trolls by Twitter. On the other hand, we find that on average, troll accounts were tens of times more influential than regular users were. Moreover, 23% and 22% of regular accounts in the top-100 and top-1000 respectively, have now been suspended by Twitter. This raises questions about their authenticity and practices during the 2016 US presidential election.

cs.SI↗

arXiv2021

My Mouse, My Rules: Privacy Issues of Behavioral User Profiling via Mouse Tracking

This paper aims to stir debate about a disconcerting privacy issue on web browsing that could easily emerge because of unethical practices and uncontrolled use of technology. We demonstrate how straightforward is to capture behavioral data about the users at scale, by unobtrusively tracking their mouse cursor movements, and predict user's demographics information with reasonable accuracy using five lines of code. Based on our results, we propose an adversarial method to mitigate user profiling techniques that make use of mouse cursor tracking, such as the recurrent neural net we analyze in this paper. We also release our data and a web browser extension that implements our adversarial method, so that others can benefit from this work in practice.

cs.HC↗

arXiv2020

Design of a Near-Ideal Fault-Tolerant Routing Algorithm for Network-on-Chip-Based Multicores

With relentless CMOS technology downsizing Networks-on-Chips (NoCs) are inescapably experiencing escalating susceptibility to wearout and reduced reliability. While faults in processors and memories may be masked via redundancy, or mitigated via techniques such as task migration, NoCs are especially vulnerable to hardware faults as a single link breakdown may cause inter-tile communication to halt indefinitely, rendering the whole multicore chip inoperable. As such, NoCs impose the risk of becoming the pivotal point of failure in chip multicores that utilize them. Aiming towards seamless NoC operation in the presence of faulty links we propose Hermes, a near-ideal fault-tolerant routing algorithm that meets the objectives of exhibiting high levels of robustness, operating in a distributed mode, guaranteeing freedom from deadlocks, and evening-out traffic, among many. Hermes is a limited-overhead deadlock-free hybrid routing algorithm, utilizing load-balancing routing on fault-free paths to sustain high-throughput, while providing pre-reconfigured escape path selection in the vicinity of faults. Under such online mechanisms, Hermes's performance degrades gracefully with increasing faulty link counts, a crucially desirable response lacking in prior-art. Additionally, Hermes identifies non-communicating network partitions in scenarios where faulty links are topologically densely distributed such that packets being routed to physically isolated regions cause no network stagnation due to indefinite chained blockages starting at sub-network boundaries. An extensive experimental evaluation, including utilizing traffic workloads gathered from full-system chip multi-processor simulations, shows that Hermes improves network throughput by up to $3\times$ when compared against the state-of-the-art. Further, hardware synthesis results prove Hermes's efficacy.

cs.AR↗

arXiv2019

Who's Tracking Sensitive Domains?

We turn our attention to the elephant in the room of data protection, which is none other than the simple and obvious question: "Who's tracking sensitive domains?". Despite a fast-growing amount of work on more complex facets of the interplay between privacy and the business models of the Web, the obvious question of who collects data on domains where most people would prefer not be seen, has received rather limited attention. First, we develop a methodology for automatically annotating websites that belong to a sensitive category, e.g. as defined by the General Data Protection Regulation (GDPR). Then, we extract the third party tracking services included directly, or via recursive inclusions, by the above mentioned sites. Having analyzed around 30k sensitive domains, we show that such domains are tracked, albeit less intensely than the mainstream ones. Looking in detail at the tracking services operating on them, we find well known names, as well as some less known ones, including some specializing on specific sensitive categories.

cs.CR↗

arXiv2019

Beyond content analysis: Detecting targeted ads via distributed counting

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 iWnder 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 jeopardising their privacy. We break this deadlock with a privacy preserving data sharing protocol that allows iWnder 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, iWnder 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.

cs.CY↗