Social Turing Tests: Crowdsourcing Sybil Detection

As popular tools for spreading spam and malware, Sybils (or fake accounts) pose a serious threat to online communities such as Online Social Networks (OSNs). Today, sophisticated attackers are creating realistic Sybils that effectively befriend legitimate users, rendering most automated Sybil detection techniques ineffective. In this paper, we explore the feasibility of a crowdsourced Sybil detection system for OSNs. We conduct a large user study on the ability of humans to detect today's Sybil accounts, using a large corpus of ground-truth Sybil accounts from the Facebook and Renren networks. We analyze detection accuracy by both "experts" and "turkers" under a variety of conditions, and find that while turkers vary significantly in their effectiveness, experts consistently produce near-optimal results. We use these results to drive the design of a multi-tier crowdsourcing Sybil detection system. Using our user study data, we show that this system is scalable, and can be highly effective either as a standalone system or as a complementary technique to current tools

LCT: A Lightweight Cross-domain Trust Model for the Mobile Distributed Environment

In the mobile distributed environment, an entity may move across domains with great frequency. How to utilize the trust information in the previous domains and quickly establish trust relationships with others in the current domain remains a challenging issue. The classic trust models do not support cross-domain and the existing cross-domain trust models are not in a fully distributed way

TOSI: a trust-oriented social influence evaluation method in contextual social networks

Online Social Networks (OSNs) have been used as the means for a variety of applications. For example, social networking platform has been used in employment system, e-Commerce and CRM system to improve the quality of recommendations with the assistance of social networks. In these applications, social influence acts as a significant role, affecting people's decision-making. However, the existing social influence evaluation methods do not fully consider the social contexts, i.e., the social relationships and the social trust between participants, and the preferences of participants, which have significant impact on social influence evaluation in OSNs. Thus, these existing methods cannot deliver accurate social influence evaluation results. In our paper, we propose a Trust-Oriented Social Influence evaluation method, called TOSI, with taking the social contexts into account. We conduct experiments onto two real social network datasets, i.e., Epinions and DBLP. The experimental results illustrate that our TOSI method greatly outperforms the state-of-the-art method SoCap in terms of effectiveness, efficiency and robustness

Εξερευνώντας μονοπάτια εμπιστοσύνης

Στα πλαίσια αυτής της διπλωματικής εργασίας μελετήσαμε την έννοια της εμπιστοσύνης στους διαδικτυακούς τόπους, καθώς και τα μοντέλα που έχουν αναπτυχθεί σε διαφορετικούς τύπους δικτύων και ασχολούνται με τον τρόπο διάδοσης της εμπιστοσύνης σε αυτά. Στη συνέχεια αναλύσαμε και τοποθετήσαμε τις διαφορετικές μεθόδους κάτω από ένα ενιαίο πλαίσιο μοντελοποίησης ακολουθώντας τους φορμαλισμούς και τις έννοιες της άλγεβρας μονοπατιών. Στα πειράματα που πραγματοποιήσαμε, επιλέξαμε τρεις αλγορίθμους διαφορετικών μοντέλων εμπιστοσύνης και τους υλοποιήσαμε, τόσο στην αρχική τους μορφή όσο και με βάση τη δική μας προτεινόμενη μοντελοποίηση, για να τα συγκρίνουμε ως προς την ταχύτητα στο δίκτυο Epinions. Τα τρία αυτά μοντέλα χρησιμοποιήθηκαν περαιτέρω και για την πραγματοποίηση έρευνας που διενεργήθηκε με ερευνητές που εργάζονται στο τμήμα Πληροφορικής και Τηλεπικοινωνιών του ΕΚΠΑ. Και στα δύο δίκτυα έγιναν πειράματα εκτίμησης λάθους πρόβλεψης εμπιστοσύνης των τριών μοντέλων για να γίνει εφικτή η ποιοτική σύγκριση του μηχανισμού μετάδοσης που χρησιμοποιούν.In the context of this thesis we studied the notion of trust in various networking sites, as well as the types of trust models that have developed over the years in different networks and deal with the propagation of trust. We analyzed the various methods and place them under a unique modeling framework that follows the formalisms and main concepts of a path algebra formalism. In our experiments we chose three algorithms that model trust differently and implemented them both in their original form and in their corresponding variations that we have proposed. Given a sample of the Epinions network, a comparison is made on the running speed of these algorithms. Furthermore, those models were adopted to perform a survey with researchers working at the Department of Informatics and Telecommunications in NKUA. In both networks we performed experiments that deduced the average prediction error so as to compare the models in the qualitative of the predictions that their propagation mechanisms produce

Networks and trust: systems for understanding and supporting internet security

Includes bibliographical references.2022 Fall.This dissertation takes a systems-level view of the multitude of existing trust management systems to make sense of when, where and how (or, in some cases, if) each is best utilized. Trust is a belief by one person that by transacting with another person (or organization) within a specific context, a positive outcome will result. Trust serves as a heuristic that enables us to simplify the dozens decisions we make each day about whom we will transact with. In today's hyperconnected world, in which for many people a bulk of their daily transactions related to business, entertainment, news, and even critical services like healthcare take place online, we tend to rely even more on heuristics like trust to help us simplify complex decisions. Thus, trust plays a critical role in online transactions. For this reason, over the past several decades researchers have developed a plethora of trust metrics and trust management systems for use in online systems. These systems have been most frequently applied to improve recommender systems and reputation systems. They have been designed for and applied to varied online systems including peer-to-peer (P2P) filesharing networks, e-commerce platforms, online social networks, messaging and communication networks, sensor networks, distributed computing networks, and others. However, comparatively little research has examined the effects on individuals, organizations or society of the presence or absence of trust in online sociotechnical systems. Using these existing trust metrics and trust management systems, we design a set of experiments to benchmark the performance of these existing systems, which rely heavily on network analysis methods. Drawing on the experiments' results, we propose a heuristic decision-making framework for selecting a trust management system for use in online systems. In this dissertation we also investigate several related but distinct aspects of trust in online sociotechnical systems. Using network/graph analysis methods, we examine how trust (or lack of trust) affects the performance of online networks in terms of security and quality of service. We explore the structure and behavior of online networks including Twitter, GitHub, and Reddit through the lens of trust. We find that higher levels of trust within a network are associated with more spread of misinformation (a form of cybersecurity threat, according to the US CISA) on Twitter. We also find that higher levels of trust in open source developer networks on GitHub are associated with more frequent incidences of cybersecurity vulnerabilities. Using our experimental and empirical findings previously described, we apply the Systems Engineering Process to design and prototype a trust management tool for use on Reddit, which we dub Coni the Trust Moderating Bot. Coni is, to the best of our knowledge, the first trust management tool designed specifically for use on the Reddit platform. Through our work with Coni, we develop and present a blueprint for constructing a Reddit trust tool which not only measures trust levels, but can use these trust levels to take actions on Reddit to improve the quality of submissions within the community (a subreddit)

A Trust Management Framework for Decision Support Systems

In the era of information explosion, it is critical to develop a framework which can extract useful information and help people to make “educated” decisions. In our lives, whether we are aware of it, trust has turned out to be very helpful for us to make decisions. At the same time, cognitive trust, especially in large systems, such as Facebook, Twitter, and so on, needs support from computer systems. Therefore, we need a framework that can effectively, but also intuitively, let people express their trust, and enable the system to automatically and securely summarize the massive amounts of trust information, so that a user of the system can make “educated” decisions, or at least not blind decisions. Inspired by the similarities between human trust and physical measurements, this dissertation proposes a measurement theory based trust management framework. It consists of three phases: trust modeling, trust inference, and decision making. Instead of proposing specific trust inference formulas, this dissertation proposes a fundamental framework which is flexible and can be adapted by many different inference formulas. Validation experiments are done on two data sets: the Epinions.com data set and the Twitter data set. This dissertation also adapts the measurement theory based trust management framework for two decision support applications. In the first application, the real stock market data is used as ground truth for the measurement theory based trust management framework. Basically, the correlation between the sentiment expressed on Twitter and stock market data is measured. Compared with existing works which do not differentiate tweets’ authors, this dissertation analyzes trust among stock investors on Twitter and uses the trust network to differentiate tweets’ authors. The results show that by using the measurement theory based trust framework, Twitter sentiment valence is able to reflect abnormal stock returns better than treating all the authors as equally important or weighting them by their number of followers. In the second application, the measurement theory based trust management framework is used to help to detect and prevent from being attacked in cloud computing scenarios. In this application, each single flow is treated as a measurement. The simulation results show that the measurement theory based trust management framework is able to provide guidance for cloud administrators and customers to make decisions, e.g. migrating tasks from suspect nodes to trustworthy nodes, dynamically allocating resources according to trust information, and managing the trade-off between the degree of redundancy and the cost of resources