Defending against Sybil Devices in Crowdsourced Mapping Services

Real-time crowdsourced maps such as Waze provide timely updates on traffic, congestion, accidents and points of interest. In this paper, we demonstrate how lack of strong location authentication allows creation of software-based {\em Sybil devices} that expose crowdsourced map systems to a variety of security and privacy attacks. Our experiments show that a single Sybil device with limited resources can cause havoc on Waze, reporting false congestion and accidents and automatically rerouting user traffic. More importantly, we describe techniques to generate Sybil devices at scale, creating armies of virtual vehicles capable of remotely tracking precise movements for large user populations while avoiding detection. We propose a new approach to defend against Sybil devices based on {\em co-location edges}, authenticated records that attest to the one-time physical co-location of a pair of devices. Over time, co-location edges combine to form large {\em proximity graphs} that attest to physical interactions between devices, allowing scalable detection of virtual vehicles. We demonstrate the efficacy of this approach using large-scale simulations, and discuss how they can be used to dramatically reduce the impact of attacks against crowdsourced mapping services.Comment: Measure and integratio

Disruption and Deception in Crowdsourcing: Towards a Crowdsourcing Risk Framework

While crowdsourcing has become increasingly popular among organizations, it also has become increasingly susceptible to unethical and malicious activities. This paper discusses recent examples of disruptive and deceptive efforts on crowdsourcing sites, which impacted the confidentiality, integrity, and availability of the crowdsourcing efforts’ service, stakeholders, and data. From these examples, we derive an organizing framework of risk types associated with disruption and deception in crowdsourcing based on commonalities among incidents. The framework includes prank activities, the intentional placement of false information, hacking attempts, DDoS attacks, botnet attacks, privacy violation attempts, and data breaches. Finally, we discuss example controls that can assist in identifying and mitigating disruption and deception risks in crowdsourcing

Assessing the Impacts of Crowdsourcing in Logistics and Supply Chain Operations

Crowdsourcing models, whereby firms start to delegate supply chain operations activities to a mass of actors in the marketplace, have grown drastically in recent years. 85% of the top global brands have reported to use crowdsourcing in the last ten year with top names such as Procter & Gamble, Unilever, and Nestle. These emergent business models, however, have remained unexplored in extant SCM literature. Drawing on various theoretical underpinnings, this dissertation aims to investigate and develop a holistic understanding of the importance and impacts of crowdsourcing in SCM from multiple perspectives. Three individual studies implementing a range of methodological approaches (archival data, netnography, and field and scenario-based experiments) are conducted to examine potential impacts of crowdsourcing in different supply chain processes from the customer’s, the crowdsourcing firm’s, and the supply chain partner’s perspectives. Essay 1 employs a mixed method approach to investigate “how, when, and why” crowdsourced delivery may affect customer satisfaction and behavioral intention in online retailing. Essay 2 uses a field experiment to address how the framing of motivation messages could enhance crowdsourced agents’ participation and performance level in crowdsourced inventory audit tasks. Lastly, Essay 3 explores the impact of crowdsourcing activities by the manufacturers on the relationship dynamics within the manufacturer-consumers-retailer triads

Web-Based VR Experiments Powered by the Crowd

We build on the increasing availability of Virtual Reality (VR) devices and Web technologies to conduct behavioral experiments in VR using crowdsourcing techniques. A new recruiting and validation method allows us to create a panel of eligible experiment participants recruited from Amazon Mechanical Turk. Using this panel, we ran three different crowdsourced VR experiments, each reproducing one of three VR illusions: place illusion, embodiment illusion, and plausibility illusion. Our experience and worker feedback on these experiments show that conducting Web-based VR experiments using crowdsourcing is already feasible, though some challenges---including scale---remain. Such crowdsourced VR experiments on the Web have the potential to finally support replicable VR experiments with diverse populations at a low cost.Comment: The Web Conference 2018 (WWW 2018); update citation forma

Stateless Puzzles for Real Time Online Fraud Preemption

The profitability of fraud in online systems such as app markets and social networks marks the failure of existing defense mechanisms. In this paper, we propose FraudSys, a real-time fraud preemption approach that imposes Bitcoin-inspired computational puzzles on the devices that post online system activities, such as reviews and likes. We introduce and leverage several novel concepts that include (i) stateless, verifiable computational puzzles, that impose minimal performance overhead, but enable the efficient verification of their authenticity, (ii) a real-time, graph-based solution to assign fraud scores to user activities, and (iii) mechanisms to dynamically adjust puzzle difficulty levels based on fraud scores and the computational capabilities of devices. FraudSys does not alter the experience of users in online systems, but delays fraudulent actions and consumes significant computational resources of the fraudsters. Using real datasets from Google Play and Facebook, we demonstrate the feasibility of FraudSys by showing that the devices of honest users are minimally impacted, while fraudster controlled devices receive daily computational penalties of up to 3,079 hours. In addition, we show that with FraudSys, fraud does not pay off, as a user equipped with mining hardware (e.g., AntMiner S7) will earn less than half through fraud than from honest Bitcoin mining

Crowdsourcing geospatial data for Earth and human observations: a review

The transformation from authoritative to user-generated data landscapes has garnered considerable attention, notably with the proliferation of crowdsourced geospatial data. Facilitated by advancements in digital technology and high-speed communication, this paradigm shift has democratized data collection, obliterating traditional barriers between data producers and users. While previous literature has compartmentalized this subject into distinct platforms and application domains, this review offers a holistic examination of crowdsourced geospatial data. Employing a narrative review approach due to the interdisciplinary nature of the topic, we investigate both human and Earth observations through crowdsourced initiatives. This review categorizes the diverse applications of these data and rigorously examines specific platforms and paradigms pertinent to data collection. Furthermore, it addresses salient challenges, encompassing data quality, inherent biases, and ethical dimensions. We contend that this thorough analysis will serve as an invaluable scholarly resource, encapsulating the current state-of-the-art in crowdsourced geospatial data, and offering strategic directions for future interdisciplinary research and applications across various sectors

Speeching: Mobile Crowdsourced Speech Assessment to Support Self-Monitoring and Management for People with Parkinson's

We present Speeching, a mobile application that uses crowdsourcing to support the self-monitoring and management of speech and voice issues for people with Parkinson's (PwP). The application allows participants to audio record short voice tasks, which are then rated and assessed by crowd workers. Speeching then feeds these results back to provide users with examples of how they were perceived by listeners unconnected to them (thus not used to their speech patterns). We conducted our study in two phases. First we assessed the feasibility of utilising the crowd to provide ratings of speech and voice that are comparable to those of experts. We then conducted a trial to evaluate how the provision of feedback, using Speeching, was valued by PwP. Our study highlights how applications like Speeching open up new opportunities for self-monitoring in digital health and wellbeing, and provide a means for those without regular access to clinical assessment services to practice-and get meaningful feedback on-their speech