Revisiting Content Availability in Distributed Online Social Networks

Online Social Networks (OSN) are among the most popular applications in today's Internet. Decentralized online social networks (DOSNs), a special class of OSNs, promise better privacy and autonomy than traditional centralized OSNs. However, ensuring availability of content when the content owner is not online remains a major challenge. In this paper, we rely on the structure of the social graphs underlying DOSN for replication. In particular, we propose that friends, who are anyhow interested in the content, are used to replicate the users content. We study the availability of such natural replication schemes via both theoretical analysis as well as simulations based on data from OSN users. We find that the availability of the content increases drastically when compared to the online time of the user, e. g., by a factor of more than 2 for 90% of the users. Thus, with these simple schemes we provide a baseline for any more complicated content replication scheme.Comment: 11pages, 12 figures; Technical report at TU Berlin, Department of Electrical Engineering and Computer Science (ISSN 1436-9915

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

Socially responsible design for social robots in public spaces

University of Technology Sydney. Faculty of Engineering and Information Technology.Social robots are an innovative technology using artificial intelligence (AI), that can combine social, physical and digital interactions to create unique user experiences. Potential applications require a good user experience to encourage adoption by people. Social robots can also be considered surveillance devices, with sensors, image recording and AI that can identify faces and emotions. Therefore, organisations deploying social robot applications must meet privacy legislation requirements regarding the personal data used and collected in an interaction. Furthermore, to be socially responsible, organisations must behave in a manner that benefits society. Hence deploying a social robot in a public space must be carefully managed for the impact it might have on people within that environment. Design methods, that incorporate privacy considerations for public spaces and that enable a good user experience, are not yet available. This dissertation contributes a design framework for organisations, that enables discovering potential applications, designing these applications with consideration for both privacy and user experience, and implementing these applications for use with humanoid social robots in public spaces. This research used an Action Design Research (ADR) approach to develop methods that allow organisations to discover and implement socially responsible social robot applications, incorporating purposefully designed User Experience (UX) and privacy considerations, in public spaces. Embodying ADR principles of practice-inspired research and theory-ingrained artifact, two experiments were firstly undertaken. The first experiment indicated that a social robot could function as a social agent, effectively undertaking a task requiring social engagement. However, greater value for organisations might be realised through applications utilising the full capabilities of a social robot across the social, physical and digital realms. The second experiment investigated privacy and UX, discovering that people may provide more personal information to a humanoid robot than to a kiosk when using transparency, which is a core component of Privacy-by-Design. Both experiments contributed to the development of a UX Human-Robot Interaction (HRI) design framework for social robot applications, that combines Lean UX (composed of Lean Startup, Design Thinking and Agile practices), privacy theory and HRI theory. This UX-HRI framework was refined in iterative cycles of building, testing and evaluating social robot applications with design studies in three different environments, using the ADR principles of reciprocal shaping, mutually influential roles, and authentic and concurrent evaluation. Design principles were formulated through the generalisation of the context-specific findings. Guided by this UX-HRI design framework, socially responsible social robot applications can be created

A Regulatory Model for Context-Aware Abstract Framework

Proceedings of: 23rd International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2010, Cordoba, Spain, June 1-4, 2010.This paper presents a general framework to define a context aware application and analyzes social guarantees to be considered to develop this kind of applications following legal assumptions as privacy, human rights, etc. We present a review of legal issues in biometric user identification where several legal aspects have been developed in European Union regulation and a general framework to define context aware applications. As main result, paper presents a legal framework to be taken into account in any context-based application to ensure a harmonious and coherent system for the protection of fundamental rights.This work was supported in part by Projects CICYT TIN2008-06742-C02-02/TSI, CICYT TEC2008-06732-C02-02/TEC, CAM CONTEXTS (S2009/TIC-1485) and DPS2008-07029- C02-02.Publicad

Can a five minute, three question survey foretell first-year engineering student performance and retention?

This research paper examines first-year student performance and retention within engineering. A considerable body of literature has reported factors influencing performance and retention, including high school GPA and SAT scores,1,2,3 gender,4 self-efficacy,1,5 social status,2,6,7 hobbies,4 and social integration.6,7 Although these factors can help explain and even partially predict student outcomes, they can be difficult to measure; typical survey instruments are lengthy and can be invasive of student privacy. To address this limitation, the present paper examines whether a much simpler survey can be used to understand student motivations and anticipate student outcomes. The survey was administered to 347 students in an introductory Engineering Graphics and Design course. At the beginning of the first day of class, students were given a three-question, open-ended questionnaire that asked: “In your own words, what do engineers do?”, “Why did you choose engineering?”, and “Was there any particular person or experience that influenced your decision?” Two investigators independently coded the responses, identifying dozens of codes for both motivations for pursuing engineering and understanding of what it is. Five hypotheses derived from Dweck’s mindset theory7 and others8,9 were tested to determine if particular codes were predictive of first-semester GPA or first-year retention in engineering. Codes that were positively and significantly associated with first-semester GPA included: explaining why engineers do engineering or how they do it, stating that engineers create ideas, visions, and theories, stating that engineers use math, science, physics or analysis, and expressing enjoyment of math and science, whereas expressing interest in specific technical applications or suggesting that engineers simplify and make life easier were negatively and significantly related to first-semester GPA. Codes positively and significantly associated with first-year retention in engineering included: stating that engineers use math or that engineers design or test things, expressing enjoyment of math, science, or problem solving, and indicating any influential person who is an engineer. Codes negatively and significantly associated with retention included: citing an extrinsic motivation for pursuing engineering, stating that they were motivated by hearing stories about engineering, and stating that parents or family pushed the student to become an engineer. Although many prior studies have suggested that student self-efficacy is related to retention,1,5 this study found that student interests were more strongly associated with retention. This finding is supported by Dweck’s mindset theory: students with a “growth” mindset (e.g., “I enjoy math”) would be expected to perform better and thus be retained at a higher rate than those with a “fixed” mindset (e.g., “I am good at math”).7 We were surprised that few students mentioned activities expressly designed to stimulate interest in engineering, such as robotics competitions and high school engineering classes. Rather, they cited general interests in math, problem solving, and creativity, as well as family influences, all factors that are challenging for the engineering education community to address. These findings demonstrate that relative to its ease of administration, a five minute survey can indeed help to anticipate student performance and retention. Its minimalism enables easy implementation in an introductory engineering course, where it serves not only as a research tool, but also as a pedagogical aid to help students and teacher discover student perceptions about engineering and customize the curriculum appropriately

Federated Survival Forests

Survival analysis is a subfield of statistics concerned with modeling the occurrence time of a particular event of interest for a population. Survival analysis found widespread applications in healthcare, engineering, and social sciences. However, real-world applications involve survival datasets that are distributed, incomplete, censored, and confidential. In this context, federated learning can tremendously improve the performance of survival analysis applications. Federated learning provides a set of privacy-preserving techniques to jointly train machine learning models on multiple datasets without compromising user privacy, leading to a better generalization performance. However, despite the widespread development of federated learning in recent AI research, few studies focus on federated survival analysis. In this work, we present a novel federated algorithm for survival analysis based on one of the most successful survival models, the random survival forest. We call the proposed method Federated Survival Forest (FedSurF). With a single communication round, FedSurF obtains a discriminative power comparable to deep-learning-based federated models trained over hundreds of federated iterations. Moreover, FedSurF retains all the advantages of random forests, namely low computational cost and natural handling of missing values and incomplete datasets. These advantages are especially desirable in real-world federated environments with multiple small datasets stored on devices with low computational capabilities. Numerical experiments compare FedSurF with state-of-the-art survival models in federated networks, showing how FedSurF outperforms deep-learning-based federated algorithms in realistic environments with non-identically distributed data

Privacy in crowdsourcing:a systematic review

The advent of crowdsourcing has brought with it multiple privacy challenges. For example, essential monitoring activities, while necessary and unavoidable, also potentially compromise contributor privacy. We conducted an extensive literature review of the research related to the privacy aspects of crowdsourcing. Our investigation revealed interesting gender differences and also differences in terms of individual perceptions. We conclude by suggesting a number of future research directions.</p