How Far Removed Are You? Scalable Privacy-Preserving Estimation of Social Path Length with Social PaL

Social relationships are a natural basis on which humans make trust decisions. Online Social Networks (OSNs) are increasingly often used to let users base trust decisions on the existence and the strength of social relationships. While most OSNs allow users to discover the length of the social path to other users, they do so in a centralized way, thus requiring them to rely on the service provider and reveal their interest in each other. This paper presents Social PaL, a system supporting the privacy-preserving discovery of arbitrary-length social paths between any two social network users. We overcome the bootstrapping problem encountered in all related prior work, demonstrating that Social PaL allows its users to find all paths of length two and to discover a significant fraction of longer paths, even when only a small fraction of OSN users is in the Social PaL system - e.g., discovering 70% of all paths with only 40% of the users. We implement Social PaL using a scalable server-side architecture and a modular Android client library, allowing developers to seamlessly integrate it into their apps.Comment: A preliminary version of this paper appears in ACM WiSec 2015. This is the full versio

Fibrinogen-elongated Chain Inhibits Thrombin-induced Platelet Response, Hindering the Interaction with Different Receptors

The expression of the elongated fibrinogen γ chain, termed γ′, derives from alternative splicing of mRNA and causes an insertion sequence of 20 amino acids. This insertion domain interacts with the anion-binding exosite (ABE)-II of thrombin. This study investigated whether and how γ′ chain binding to ABE-II affects thrombin interaction with its platelet receptors, i.e. glycoprotein Ibα (GpIbα), protease-activated receptor (PAR) 1, and PAR4. Both synthetic γ′ peptide and fibrinogen fragment D*, containing the elongated γ′ chain, inhibited thrombin-induced platelet aggregation up to 70%, with IC50 values of 42 ± 3.5 and 0.47 ± 0.03 μm, respectively. Solid-phase binding and spectrofluorimetric assays showed that both fragment D* and the synthetic γ′ peptide specifically bind to thrombin ABE-II and competitively inhibit the thrombin binding to GpIbα with a mean Ki ≈ 0.5 and ≈35 μm, respectively. Both these γ′ chain-containing ligands allosterically inhibited thrombin cleavage of a synthetic PAR1 peptide, of native PAR1 molecules on intact platelets, and of the synthetic chromogenic peptide d-Phe-pipecolyl-Arg-p-nitroanilide. PAR4 cleavage was unaffected. In summary, fibrinogen γ′ chain binds with high affinity to thrombin and inhibits with combined mechanisms the platelet response to thrombin. Thus, its variations in vivo may affect the hemostatic balance in arterial circulation

Mean birds: Detecting aggression and bullying on Twitter

In recent years, bullying and aggression against social media users have grown significantly, causing serious consequences to victims of all demographics. Nowadays, cyberbullying affects more than half of young social media users worldwide, suffering from prolonged and/or coordinated digital harassment. Also, tools and technologies geared to understand and mitigate it are scarce and mostly ineffective. In this paper, we present a principled and scalable approach to detect bullying and aggressive behavior on Twitter. We propose a robust methodology for extracting text, user, and network-based attributes, studying the properties of bullies and aggressors, and what features distinguish them from regular users. We find that bullies post less, participate in fewer online communities, and are less popular than normal users. Aggressors are relatively popular and tend to include more negativity in their posts. We evaluate our methodology using a corpus of 1.6M tweets posted over 3 months, and show that machine learning classification algorithms can accurately detect users exhibiting bullying and aggressive behavior, with over 90% AUC

Measuring #GamerGate: A Tale of Hate, Sexism, and Bullying

Over the past few years, online aggression and abusive behaviors have occurred in many different forms and on a variety of platforms. In extreme cases, these incidents have evolved into hate, discrimination, and bullying, and even materialized into real-world threats and attacks against individuals or groups. In this paper, we study the Gamergate controversy. Started in August 2014 in the online gaming world, it quickly spread across various social networking platforms, ultimately leading to many incidents of cyberbullying and cyberaggression. We focus on Twitter, presenting a measurement study of a dataset of 340k unique users and 1.6M tweets to study the properties of these users, the content they post, and how they differ from random Twitter users. We find that users involved in this "Twitter war" tend to have more friends and followers, are generally more engaged and post tweets with negative sentiment, less joy, and more hate than random users. We also perform preliminary measurements on how the Twitter suspension mechanism deals with such abusive behaviors. While we focus on Gamergate, our methodology to collect and analyze tweets related to aggressive and bullying activities is of independent interest

Detecting cyberbullying and cyberaggression in social media

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

Kek, Cucks, and God Emperor Trump: A Measurement Study of 4chan's Politically Incorrect Forum and its Effects on the Web

The discussion-board site 4chan has been part of the Internet's dark underbelly since its inception, and recent political events have put it increasingly in the spotlight. In particular, /pol/, the “Politically Incorrect'” board, has been a central figure in the outlandish 2016 US election season, as it has often been linked to the alt-right movement and its rhetoric of hate and racism. However, 4chan remains relatively unstudied by the scientific community: little is known about its user base, the content it generates, and how it affects other parts of the Web. In this paper, we start addressing this gap by analyzing /pol/ along several axes, using a dataset of over 8M posts we collected over two and a half months. First, we perform a general characterization, showing that /pol/ users are well distributed around the world and that 4chan's unique features encourage fresh discussions. We also analyze content, finding, for instance, that YouTube links and hate speech are predominant on /pol/. Overall, our analysis not only provides the first measurement study of /pol/, but also insight into online harassment and hate speech trends in social media

Influence of carbon and nitrogen on electronic structure and hyperfine interactions in fcc iron-based alloys

Carbon and nitrogen austenites, modeled by Fe8N and Fe8C superstructures are studied by full-potential LAPW method. Structure parameters, electronic and magnetic properties as well as hyperfine interaction parameters are obtained. Calculations prove that Fe-C austenite can be successfully modeled by ordered Fe8C superstructure. The results show that chemical Fe-C bond in Fe8C has higher covalent part than in Fe8N. Detailed analysis of electric field gradient formation for both systems is performed. The calculation of electric field gradient allow us to carry out a good interpretation of Moessbauer spectra for Fe-C and Fe-N systems.Comment: 8 pages, 3 figures, IOP-style LaTeX, submitted to J. Phys. Condens. Matte

Flexible and Robust Privacy-Preserving Implicit Authentication

Implicit authentication consists of a server authenticating a user based on the user's usage profile, instead of/in addition to relying on something the user explicitly knows (passwords, private keys, etc.). While implicit authentication makes identity theft by third parties more difficult, it requires the server to learn and store the user's usage profile. Recently, the first privacy-preserving implicit authentication system was presented, in which the server does not learn the user's profile. It uses an ad hoc two-party computation protocol to compare the user's fresh sampled features against an encrypted stored user's profile. The protocol requires storing the usage profile and comparing against it using two different cryptosystems, one of them order-preserving; furthermore, features must be numerical. We present here a simpler protocol based on set intersection that has the advantages of: i) requiring only one cryptosystem; ii) not leaking the relative order of fresh feature samples; iii) being able to deal with any type of features (numerical or non-numerical). Keywords: Privacy-preserving implicit authentication, privacy-preserving set intersection, implicit authentication, active authentication, transparent authentication, risk mitigation, data brokers.Comment: IFIP SEC 2015-Intl. Information Security and Privacy Conference, May 26-28, 2015, IFIP AICT, Springer, to appea

MONITORAGGIO DELLA CORROSIONE MICROBIOLOGICA INDOTTA SU ACCIAI INOSSIDABILI IN AMBIENTI CONTAMINATI DA BATTERI MANGANESE OSSIDANTI

Il manganese è un elemento necessario per la vita dei microrganismi. Molti enzimi hanno una specifica necessità di tale elemento sfruttandone, per esempio, la capacità redox. Da osservazioni effettuate su componenti di impianti industriali che operano in ambienti contaminati da batteri manganese ossidanti, è emersa la presenza in superficie di un biofilm di caratteristiche viscose e di colore scuro. Le analisi eseguite sui biofilm hanno evidenziato che tali depositi sono costituiti da ossidi e idrossidi di Mn. Una parte significante delle colonie batteriche identificate hanno mostrato la capacità di produrre MnO2 (1-2). La presenza di batteri manganese-ossidanti produce negli acciai inossidabili un tipico innalzamento del potenziale di corrosione. Questo spostamento verso valori più nobili, rende più aggressive le condizioni di corrosione a causa dell’avvicinamento del potenziale al potenziale di pitting dell’acciaio. Nel presente lavoro è stata monitorata la corrosione microbiologica di alcuni acciai inossidabili. I test sono stati condotti su acciai austenitici e austenoferritici, in campo e in acqua di fiume sintetica contaminata da batteri manganese ossidanti, utilizzando tecniche microbiologiche ed elettrochimich