Supporting use of evidence in argumentation through practice in argumentation and reflection in the context of SOCRATES learning environment

The aim of this study was to examine how students used evidence in argumentation while they engaged in argumentive and reflective activities in the context of a designed learning environment. A web-based learning environment, SOCRATES, was developed, which included a rich data base on the topic of Climate Change. Sixteen 11th graders, working with a partner, engaged in electronic argumentive dialogs with classmates who held an opposing view on the topic and in some evidence-focused reflective activities, based on transcriptions of their dialogs. Another sixteen 11th graders, who studied the data base in the learning environment for the same amount of time as experimental-condition students but did not engage in an argumentive discourse activity, served as a comparison condition. Students who engaged in an evidence-focused dialogic intervention increased the use of evidence in their dialogs, used more evidence that functioned to weaken opponents’ claims and used more accurate evidence. Significant gains in evidence use and in meta-level communication about evidence were observed after students engaged in reflective activities. We frame our discussion of these findings in terms of their implications for promoting use of evidence in argumentation, and in relation to the development of epistemological understanding in science

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 in complex networks. 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 anti-persistent or 'mean-reverting' with short-term memory, 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

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

E-commerce shield (e-CS): a large-scale distributed price discrimination tracing system.

Personalization is a key feature of the Web, which has facilitated rich user - and context - specific services. Many companies use personalization to improve their services, such as, search engine providers and web advertising networks. Differentiating the information presented to users based on context and history is often desirable, however in most cases the users are unable to have control over how this differentiation is enabled and how it is manifested. The main concern is that personalization is not limited to search engines results and advertisements. Recent research results have detected evidence that the tracking infrastructure employed for personalization and targeted advertising is also used to deploy price discrimination on various e-commerce web-sites. The first step towards personalization is to be able to compile a detailed user profile. Profiling users is a complex task that requires collaboration within a network of third party domains and free services that exchange information based on digital traces. For researchers, these profiling networks are black boxes, because they don't have access to their internal algorithms and the type of personal information they collect. The only way to infer how these networks operate is to control their input and then observe the output that they produce (filtered search results, products price difference, targeted ads, etc.). To get a better insight on how this tracking infrastructure monitors Internet users and builds their profiles we propose a large scale distributed system that attempts to reveal common data sharing practices among different domains. The system helps users to detect price discrimination on a large number of online stores. The system is designed based on an open architecture so that it can easily evolve over time offering more privacy control to the end-user. In addition, the system encourages users to contribute information to the system, thus setting the foundations to create a large community around it. In this thesis we describe the design and implementation of this system, presenting its objectives, architecture, key components, and future extensions.Complete

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

What do information centric networks, trusted execution environments, and digital watermarking have to do with privacy, the data economy, and their future?

What if instead of having to implement controversial user tracking techniques, Internet advertising & marketing companies asked explicitly to be granted access to user data by name and category, such as Alice?Mobility?05-11-2020? The technology for implementing this already exists, and is none other than the Information Centric Networks (ICN), developed for over a decade in the framework of Next Generation Internet (NGI) initiatives. Beyond named access to personal data, ICN’s in-network storage capability can be used as a substrate for retrieving aggregated, anonymized data, or even for executing complex analytics within the network, with no personal data leaking outside. In this opinion article we discuss how ICNs combined with trusted execution environments and digital watermarking, can be combined to build a personal data overlay inter-network in which users will be able to control who gets access to their personal data, know where each copy of said data is, negotiate payments in exchange for data, and even claim ownership, and establish accountability for data leakages due to malfunctions or malice. Of course, coming up with concrete designs about how to achieve all the above will require a huge effort from a dedicated community willing to change how personal data are handled on the Internet. Our hope is that this opinion article can plant some initial seeds towards this direction

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

Understanding the Price of Data in Commercial Data Marketplaces

A large number of Data Marketplaces (DMs) have appeared in the last few years to help owners monetize their data, and data buyers optimize their marketing campaigns, train their ML models, and facilitate other data-driven decision processes. In this paper, we present a first of its kind measurement study of the growing DM ecosystem, focused on understanding which features of data are actually driving their prices in the market. We show that data products listed in commercial DMs may cost from few to hundreds of thousands of US dollars. We analyze 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 data products across different DMs, as well as a regression analysis for revealing features that correlate with data product prices of specific categories, such as update rate or history for financial data, and volume and geographical scope for marketing data