Community Networks and Sustainability: a Survey of Perceptions, Practices, and Proposed Solutions

Community network (CN) initiatives have been around for roughly two decades, evangelizing a distinctly different paradigm for building, maintaining, and sharing network infrastructure but also defending the basic human right to Internet access. Over this time they have evolved into a mosaic of systems that vary widely with respect to their network technologies, their offered services, their organizational structure, and the way they position themselves in the overall telecommunications’ ecosystem. Common to all these highly differentiated initiatives is the sustainability challenge. We approach sustainability as a broad term with an economical, political, and cultural context. We first review the different perceptions of the term. These vary both across and within the different types of stakeholders involved in CNs and are reflected in their motivation to join such initiatives. Then, we study the diverse approaches of CN operators towards the sustainability goal. Given the rich context of the term, these range all the way from mechanisms to fund their activities, to organizational structures and social activities serving as incentives for the engagement of their members. We iterate on incentive mechanisms that have been proposed and theoretically analyzed in the literature for CNs as well as tools and processes that have been actually implemented in them. Finally, we enumerate lessons that have been learned out of these two decades of CNs’ operation and discuss additional technological and regulatory issues that are key to their longer-term sustainability

Differentially Private Mobile Crowd Sensing Considering Sensing Errors

An increasingly popular class of software known as participatory sensing, or mobile crowdsensing, is a means of collecting people’s surrounding information via mobile sensing devices. To avoid potential undesired side effects of this data analysis method, such as privacy violations, considerable research has been conducted over the last decade to develop participatory sensing that looks to preserve privacy while analyzing participants’ surrounding information. To protect privacy, each participant perturbs the sensed data in his or her device, then the perturbed data is reported to the data collector. The data collector estimates the true data distribution from the reported data. As long as the data contains no sensing errors, current methods can accurately evaluate the data distribution. However, there has so far been little analysis of data that contains sensing errors. A more precise analysis that maintains privacy levels can only be achieved when a variety of sensing errors are considered

Towards trustworthy social computing systems

The rising popularity of social computing systems has managed to attract rampant forms of service abuse that negatively affects the sustainability of these systems and degrades the quality of service experienced by their users. The main factor that enables service abuse is the weak identity infrastructure used by most sites, where identities are easy to create with no verification by a trusted authority. Attackers are exploiting this infrastructure to launch Sybil attacks, where they create multiple fake (Sybil) identities to take advantage of the combined privileges associated with the identities to abuse the system. In this thesis, we present techniques to mitigate service abuse by designing and building defense schemes that are robust and practical. We use two broad defense strategies: (1) Leveraging the social network: We first analyze existing social network-based Sybil detection schemes and present their practical limitations when applied on real world social networks. Next, we present an approach called Sybil Tolerance that bounds the impact an attacker can gain from using multiple identities; (2) Leveraging activity history of identities: We present two approaches, one that applies anomaly detection on user social behavior to detect individual misbehaving identities, and a second approach called Stamper that focuses on detecting a group of Sybil identities. We show that both approaches in this category raise the bar for defense against adaptive attackers.Die steigende Popularität sozialer Medien führt zu umfangreichen Missbrauch mit negativen Folgen für die nachhaltige Funktionalität und verringerter Qualität des Services. Der Missbrauch wird maßgeblich durch die Nutzung schwacher Identifikationsverfahren, die eine einfache Anmeldung ohne Verifikation durch eine vertrauenswürdige Behörde erlaubt, ermöglicht. Angreifer nutzen diese Umgebung aus und attackieren den Service mit sogenannten Sybil Angriffen, bei denen mehrere gefälschte (Sybil) Identitäten erstellt werden, um einen Vorteil durch die gemeinsamen Privilegien der Identitäten zu erhalten und den Service zu missbrauchen. Diese Doktorarbeit zeigt Techniken zur Verhinderung von Missbrauch sozialer Medien, in dem Verteidigungsmechanismen konstruiert und implementiert werden, die sowohl robust als auch praktikabel sind. Zwei Verteidigungsstrategien werden vorgestellt: (1) Unter Ausnutzung des sozialen Netzwerks: Wir analysieren zuerst existierende soziale Netzwerk-basierende Sybil Erkennungsmechanismen und zeigen deren praktische Anwendungsgrenzen auf bei der Anwendung auf soziale Netzwerke aus der echten Welt. Im Anschluss zeigen wir den Ansatz der sogenannten Sybil Toleranz, welcher die Folgen eines Angriffs mit mehreren Identitäten einschränkt. (2) Unter Ausnutzung des Aktivitätsverlaufs von Identitäten: Wir präsentieren zwei Ansätze, einen anwendbar für die Erkennung von Unregelmäßigkeiten in dem sozialen Verhalten eines Benutzers zur Erkennung unanständiger Benutzer und ein weiterer Ansatz namens Stamper, dessen Fokus die Erkennung von Gruppen bestehend aus Sybil Identitäten ist. Beide gezeigten Ansätze erschweren adaptive Angriffe und verbessern existierende Verteidigungsmechanismen

Towards trustworthy social computing systems

The rising popularity of social computing systems has managed to attract rampant forms of service abuse that negatively affects the sustainability of these systems and degrades the quality of service experienced by their users. The main factor that enables service abuse is the weak identity infrastructure used by most sites, where identities are easy to create with no verification by a trusted authority. Attackers are exploiting this infrastructure to launch Sybil attacks, where they create multiple fake (Sybil) identities to take advantage of the combined privileges associated with the identities to abuse the system. In this thesis, we present techniques to mitigate service abuse by designing and building defense schemes that are robust and practical. We use two broad defense strategies: (1) Leveraging the social network: We first analyze existing social network-based Sybil detection schemes and present their practical limitations when applied on real world social networks. Next, we present an approach called Sybil Tolerance that bounds the impact an attacker can gain from using multiple identities; (2) Leveraging activity history of identities: We present two approaches, one that applies anomaly detection on user social behavior to detect individual misbehaving identities, and a second approach called Stamper that focuses on detecting a group of Sybil identities. We show that both approaches in this category raise the bar for defense against adaptive attackers.Die steigende Popularität sozialer Medien führt zu umfangreichen Missbrauch mit negativen Folgen für die nachhaltige Funktionalität und verringerter Qualität des Services. Der Missbrauch wird maßgeblich durch die Nutzung schwacher Identifikationsverfahren, die eine einfache Anmeldung ohne Verifikation durch eine vertrauenswürdige Behörde erlaubt, ermöglicht. Angreifer nutzen diese Umgebung aus und attackieren den Service mit sogenannten Sybil Angriffen, bei denen mehrere gefälschte (Sybil) Identitäten erstellt werden, um einen Vorteil durch die gemeinsamen Privilegien der Identitäten zu erhalten und den Service zu missbrauchen. Diese Doktorarbeit zeigt Techniken zur Verhinderung von Missbrauch sozialer Medien, in dem Verteidigungsmechanismen konstruiert und implementiert werden, die sowohl robust als auch praktikabel sind. Zwei Verteidigungsstrategien werden vorgestellt: (1) Unter Ausnutzung des sozialen Netzwerks: Wir analysieren zuerst existierende soziale Netzwerk-basierende Sybil Erkennungsmechanismen und zeigen deren praktische Anwendungsgrenzen auf bei der Anwendung auf soziale Netzwerke aus der echten Welt. Im Anschluss zeigen wir den Ansatz der sogenannten Sybil Toleranz, welcher die Folgen eines Angriffs mit mehreren Identitäten einschränkt. (2) Unter Ausnutzung des Aktivitätsverlaufs von Identitäten: Wir präsentieren zwei Ansätze, einen anwendbar für die Erkennung von Unregelmäßigkeiten in dem sozialen Verhalten eines Benutzers zur Erkennung unanständiger Benutzer und ein weiterer Ansatz namens Stamper, dessen Fokus die Erkennung von Gruppen bestehend aus Sybil Identitäten ist. Beide gezeigten Ansätze erschweren adaptive Angriffe und verbessern existierende Verteidigungsmechanismen

Elicitation and Aggregation of Crowd Information

This thesis addresses challenges in elicitation and aggregation of crowd information for settings where an information collector, called center, has a limited knowledge about information providers, called agents. Each agent is assumed to have noisy private information that brings a high information gain to the center when it is aggregated with the private information of other agents. We address two particular issues in eliciting crowd information: 1) how to incentivize agents to participate and provide accurate data; 2) how to aggregate crowd information so that the negative impact of agents who provide low quality information is bounded. We examine three different information elicitation settings. In the first elicitation setting, agents report their observations regarding a single phenomenon that represents an abstraction of a crowdsourcing task. The center itself does not observe the phenomenon, so it rewards agents by comparing their reports. Clearly, a rational agent bases her reporting strategy on what she believes about other agents, called peers. We prove that, in general, no payment mechanism can achieve strict properness (i.e., adopt truthful reporting as a strict equilibrium strategy) if agents only report their observations, even if they share a common belief system. This motivates the use of payment mechanisms that are based on an additional report. We show that a general payment mechanism cannot have a simple structure, often adopted by prior work, and that in the limit case, when observations can take real values, agents are constrained to share a common belief system. Furthermore, we develop several payment mechanisms for the elicitation of non-binary observations. In the second elicitation setting, a group of agents observes multiple a priori similar phenomena. Due to the a priori similarity condition, the setting represents a refinement of the former setting and enables one to achieve stronger incentive properties without requiring additional reports or constraining agents to share a common belief system. We extend the existing mechanisms to allow non-binary observations by constructing strongly truthful mechanisms (i.e., mechanisms in which truthful reporting is the highest-paying equilibrium) for different types of agents' population. In the third elicitation setting, agents observe a time evolving phenomenon, and a few of them, whose identity is known, are trusted to report truthful observations. The existence of trusted agents makes this setting much more stringent than the previous ones. We show that, in the context of online information aggregation, one can not only incentivize agents to provide informative reports, but also limit the effectiveness of malicious agents who deliberately misreport. To do so, we construct a reputation system that puts a bound on the negative impact that any misreporting strategy can have on the learned aggregate. Finally, we experimentally verify the effectiveness of novel elicitation mechanisms in community sensing simulation testbeds and a peer grading experiment

Security and Privacy in Heterogeneous Wireless and Mobile Networks: Challenges and Solutions

abstract: The rapid advances in wireless communications and networking have given rise to a number of emerging heterogeneous wireless and mobile networks along with novel networking paradigms, including wireless sensor networks, mobile crowdsourcing, and mobile social networking. While offering promising solutions to a wide range of new applications, their widespread adoption and large-scale deployment are often hindered by people's concerns about the security, user privacy, or both. In this dissertation, we aim to address a number of challenging security and privacy issues in heterogeneous wireless and mobile networks in an attempt to foster their widespread adoption. Our contributions are mainly fivefold. First, we introduce a novel secure and loss-resilient code dissemination scheme for wireless sensor networks deployed in hostile and harsh environments. Second, we devise a novel scheme to enable mobile users to detect any inauthentic or unsound location-based top-k query result returned by an untrusted location-based service providers. Third, we develop a novel verifiable privacy-preserving aggregation scheme for people-centric mobile sensing systems. Fourth, we present a suite of privacy-preserving profile matching protocols for proximity-based mobile social networking, which can support a wide range of matching metrics with different privacy levels. Last, we present a secure combination scheme for crowdsourcing-based cooperative spectrum sensing systems that can enable robust primary user detection even when malicious cognitive radio users constitute the majority.Dissertation/ThesisPh.D. Electrical Engineering 201

Disassembling online trolling: towards the better understanding and managing of online mischief-making consumer misbehaviours

This thesis draws on actor-network theory to explore the assemblages of human and nonhuman entities that allow and perpetuate online trolling. Trolling is a form of consumer misbehaviour that includes deliberate, deceptive, and mischievous attempts to provoke reactions from other online users. Despite being a pervasive online consumer misbehaviour, affecting consumers, brands, and online sites that offer a medium for trolling, trolling is poorly understood. In particular, there is a lack of understanding of what trolling actually is, how it differs from other anti-social behaviours, how it comes about, and how it could be influenced. These questions are at the forefront of this study. In disassembling trolling behaviours, this study adopts the actor-network theory (ANT) and practice-focused multi-sited ethnographic research approach. Five cases of trolling were investigated: playful trolling, good old-fashioned trolling, shock trolling, online pranking and raiding, and fake customer service trolling. Data collection included nonparticipant observation of trolling behaviours, in-depth interviews with trolls, shortelectronic exchanges with trolls and community managers, and review of trolling-related documents. Data analysis started with in-depth exploration of single actor-networks and continued with cross-case analysis, comparing and contrasting the actor-networks and building a general representation of the nature of trolling, the assemblages created in trolling, and the roles these assemblages play in the ‘doing’ of trolling. In respect of the nature of trolling, this study has found that trolling behaviours are deliberate, mischievous, deceptive, and designed to provoke a target into a reaction. Trolling behaviours benefit trolls and their followers, and they typically but not necessarily have negative consequences for the people and firms involved. These characteristics of trolling suggest that trolling should be differentiated from other online misbehaviours, in particular cyberbullying. Concerning the manifestation of trolling behaviours, this research has revealed that online trolling is performatively constituted by a collection of human and non-human entities interacting more or less in concert with each other. The study has identified nine actors participating in trolling: troll(s), target(s), medium, audience, other trolls, trolling artefacts, regulators, revenue streams, and assistants. Some of these actors (i.e., troll, target, medium) are playing a role in initiating, and other actors in sustaining trolling by celebrating it, boosting it, facilitating it, and normalising it. The findings highlight the role of other actors (apart from misbehaving consumers) in the performance of misbehaving and suggest that effective management of consumer misbehaviours such as trolling will include managing the socio-technical networks that allow and fuel these misbehaviours. Better understanding of online trolling, as an instance of online and mischief-making consumer (mis)behaviour, contributes to a more rounded understanding of consumer misbehaviours, given that prior research focused on financially motivated or illegal misbehaviours, and on misbehaving in analogue retail settings. Focusing on the act of trolling itself, this ANT-inspired thesis extends previous research on consumer misbehaviours, and trolling, which almost exclusively adopted the dispositional perspective, focusing on studying misbehaving consumers. The original contribution also lies in providing a new definition of trolling behaviours and presenting a theoretical model of how trolling comes about and is nourished. This model has practical value, providing guidance to marketers on how trolling and similar mischief-making consumer (mis)behaviours can be stymied or, if so wished, bolstered

MediaSync: Handbook on Multimedia Synchronization

This book provides an approachable overview of the most recent advances in the fascinating field of media synchronization (mediasync), gathering contributions from the most representative and influential experts. Understanding the challenges of this field in the current multi-sensory, multi-device, and multi-protocol world is not an easy task. The book revisits the foundations of mediasync, including theoretical frameworks and models, highlights ongoing research efforts, like hybrid broadband broadcast (HBB) delivery and users' perception modeling (i.e., Quality of Experience or QoE), and paves the way for the future (e.g., towards the deployment of multi-sensory and ultra-realistic experiences). Although many advances around mediasync have been devised and deployed, this area of research is getting renewed attention to overcome remaining challenges in the next-generation (heterogeneous and ubiquitous) media ecosystem. Given the significant advances in this research area, its current relevance and the multiple disciplines it involves, the availability of a reference book on mediasync becomes necessary. This book fills the gap in this context. In particular, it addresses key aspects and reviews the most relevant contributions within the mediasync research space, from different perspectives. Mediasync: Handbook on Multimedia Synchronization is the perfect companion for scholars and practitioners that want to acquire strong knowledge about this research area, and also approach the challenges behind ensuring the best mediated experiences, by providing the adequate synchronization between the media elements that constitute these experiences