No / Mobile social networks (MSNs) facilitate connections between mobile users and allow them to find other potential users who have similar interests through mobile devices, communicate with them, and benefit from their information. As MSNs are distributed public virtual social spaces, the available information may not be trustworthy to all. Therefore, mobile users are often at risk since they may not have any prior knowledge about others who are socially connected. To address this problem, trust inference plays a critical role for establishing social links between mobile users in MSNs. Taking into account the nonsemantical representation of trust between users of the existing trust models in social networks, this paper proposes a new fuzzy inference mechanism, namely MobiFuzzyTrust, for inferring trust semantically from one mobile user to another that may not be directly connected in the trust graph of MSNs. First, a mobile context including an intersection of prestige of users, location, time, and social context is constructed. Second, a mobile context aware trust model is devised to evaluate the trust value between two mobile users efficiently. Finally, the fuzzy linguistic technique is used to express the trust between two mobile users and enhance the human's understanding of trust. Real-world mobile dataset is adopted to evaluate the performance of the MobiFuzzyTrust inference mechanism. The experimental results demonstrate that MobiFuzzyTrust can efficiently infer trust with a high precision.

"Our modern society is increasingly relying on the collection, processing, and sharing of digital information. There are two fundamental trends: (1) Enabled by the rapid developments in sensor, wireless, and networking technologies, communication and networking are becoming more and more pervasive and ad hoc. (2) Driven by the explosive growth of hardware and software capabilities, computation power is becoming a public utility and information is often stored in centralized servers which facilitate ubiquitous access and sharing. Many emerging platforms and systems hinge on both dimensions, such as E-healthcare and Smart Grid. However, the majority information handled by these critical systems is usually sensitive and of high value, while various security breaches could compromise the social welfare of these systems. Thus there is an urgent need to develop security and privacy mechanisms to protect the authenticity, integrity and confidentiality of the collected data, and to control the disclosure of private information. In achieving that, two unique challenges arise: (1) There lacks centralized trusted parties in pervasive networking; (2) The remote data servers tend not to be trusted by system users in handling their data. They make existing security solutions developed for traditional networked information systems unsuitable. To this end, in this dissertation we propose a series of user-centric security and privacy mechanisms that resolve these challenging issues in untrusted network and computing environments, spanning wireless body area networks (WBAN), mobile social networks (MSN), and cloud computing. The main contributions of this dissertation are fourfold. First, we propose a secure ad hoc trust initialization protocol for WBAN, without relying on any pre-established security context among nodes, while defending against a powerful wireless attacker that may or may not compromise sensor nodes. The protocol is highly usable for a human user. Second, we present novel schemes for sharing sensitive information among distributed mobile hosts in MSN which preserves user privacy, where the users neither need to fully trust each other nor rely on any central trusted party. Third, to realize owner-controlled sharing of sensitive data stored on untrusted servers, we put forward a data access control framework using Multi-Authority Attribute-Based Encryption (ABE), that supports scalable fine-grained access and on-demand user revocation, and is free of key-escrow. Finally, we propose mechanisms for authorized keyword search over encrypted data on untrusted servers, with efficient multi-dimensional range, subset and equality query capabilities, and with enhanced search privacy. The common characteristic of our contributions is they minimize the extent of trust that users must place in the corresponding network or computing environments, in a way that is user-centric, i.e., favoring individual owners/users."

Submitted by Maria Aparecida (cidazen@gmail.com) on 2017-04-06T14:32:21Z No. of bitstreams: 1 Ariel Soares.pdf: 3082206 bytes, checksum: 46f45860ff1dce76fc8e91ec4f3546f3 (MD5) / Made available in DSpace on 2017-04-06T14:32:21Z (GMT). No. of bitstreams: 1 Ariel Soares.pdf: 3082206 bytes, checksum: 46f45860ff1dce76fc8e91ec4f3546f3 (MD5) Previous issue date: 2017-02-10 / FAPEMA, CNPQ / This research firstly investigates the privacy requirements of users in Mobile Social Networks (MSNs) through a study with 164 Brazilians, which indicated that their requirements are usually dynamic and contextual. Next, the research applies the Situational Computing paradigm to develop a solution to serve them. This solution is called SelPri, developed as proof of concept in the form of a mobile social application to autonomously adapt the privacy settings of posts in MSNs according to the user situation. SelPri uses a conceptual model with fuzzy logic as the basis for constructing an inference engine to identify mobile user situations from the following context information: location, time of the day, day of week, and co-location. SelPri is integrated with Facebook. Additionally, to show the flexibility of the conceptual model, it is also used to construct an inference engine to be used in a different application domain, the mental health. This second inference engine identifies user situations from different context information: it does not use co-location and uses the user activity. The solution originated in the mental health domain is called SituMan. Two experiments were carried out with both solutions, in order to verify the accuracy of the fuzzy inference engine to identify situations, and to evaluate the user satisfaction. The use experience evaluation with SelPri emphasized that the approach to meet the dynamic and contextdependent privacy requirements was well accepted by the participants and proved to be of practical use. The experiments also showed that both solutions were well evaluated with respect to usability. The accuracy evaluations showed a high hit rate of the inference engines to identify situations: ≈94.6% and ≈ 92.04%, for SelPri and SituMan, respectively. / Esta pesquisa primeiramente investiga os requisitos de privacidade de usuários em Redes Sociais Móveis (RSMs) através de um estudo com 164 brasileiros, o qual indicou que seus requisitos são normalmente dinâmicos e contextuais. Em seguida, a pesquisa aplica o paradigma de Computação Situacional para o desenvolvimento de uma solução para atendê-los. Esta solução é chamada de SelPri, desenvolvida como prova de conceito em forma de uma aplicação social móvel para adaptar com autonomia as configurações de privacidade de postagens em RSMs de acordo com a situação do usuário. O SelPri utiliza um modelo conceitual que faz uso de lógica nebulosa como base para a construção de um motor de inferência para identificar as situações de usuários móveis a partir das seguintes informações de contexto: localização, período do dia, dias da semana, e co-localização. O SelPri é implementado integrado ao Facebook. Adicionalmente, para mostrar a flexibilidade do modelo conceitual, ele é também usado para a construção de um motor de inferência para ser utilizado em um domínio de aplicação diferente, o de saúde mental. Esse motor de inferência identifica situações de usuários a partir de informações contextuais diferentes: não utiliza a co-localização e passa a usar a atividade do usuário. A solução originada no domínio de saúde mental é chamada de SituMan. Dois experimentos foram realizados com ambas soluções, em que objetivaram verificar a acurácia do motor de inferência nebulosa para identificação de situações, e avaliar a satisfação do usuário. A avaliação da experiência de uso realizada com o SelPri destacou que a abordagem para atender os requisitos dinâmicos e dependentes de contexto de privacidade teve uma boa aceitação pelos participantes e provou ser de uso prático. As avaliações de experiência de uso também mostraram que ambas soluções foram bem avaliadas com relação a usabilidade. As avaliações de acurácia mostraram uma taxa de acerto elevada dos motores de inferência para identificar situações: ≈94,6% e ≈92,04%, para o SelPri e SituMan, respectivamente

Submitted by JÚLIO HEBER SILVA (julioheber@yahoo.com.br) on 2016-12-02T18:19:39Z No. of bitstreams: 2 Dissertação - Fausto da Silva Moraes - 2016.pdf: 3698287 bytes, checksum: cbcf2894f028c54e60d90033a04bdc5e (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) / Approved for entry into archive by Jaqueline Silva (jtas29@gmail.com) on 2016-12-13T15:32:00Z (GMT) No. of bitstreams: 2 Dissertação - Fausto da Silva Moraes - 2016.pdf: 3698287 bytes, checksum: cbcf2894f028c54e60d90033a04bdc5e (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) / Made available in DSpace on 2016-12-13T15:32:00Z (GMT). No. of bitstreams: 2 Dissertação - Fausto da Silva Moraes - 2016.pdf: 3698287 bytes, checksum: cbcf2894f028c54e60d90033a04bdc5e (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) Previous issue date: 2016-11-09 / For the future of cellular networks is estimated a significant increase in the number of connected devices and the bulk data traffic, especially video content. That poses as a challenge for the next generation network. With the rise of new communications paradigm, D2D communication emerge as potential approach for cellular network data offloading, especially when paired with caching solutions within the network. Also, the impact of viral videos could be mitigated by proactively caching the contents being shared on Online Social Networks (OSNs). This work presents a new approach to proactive content cache for D2D-enabled networks, that is aware of users social interaction on OSN’s. Our proposal consists in the combination of user mobile and social informations to find the best located device to cache a content being shared online. Results obtained through simulation show that the proposed approach can improve offload rate, reduce energy consumption and provide a faster content access when compared with other work in literature. / As estimativas de um crescimento significativo do número de dispositivos conectados às redes sem fio e móveis, e do aumento do volume de dados trafegados, em especial o tráfego de conteúdo em vídeo, representam um desafio para a próxima geração de redes celulares. Com o surgimento de novos paradigmas de redes sem fio, a comunicação D2D (Dispositivo-a-Dispositivo) figura como uma abordagem promissora para permitir o descarregamento de dados da rede celular, principalmente quando aliada a técnicas de cache de conteúdo nos dispositivos dos usuários. Além disso, as informações de interações dos usuários em redes sociais online poderiam ser empregadas para suavizar o impacto dos vídeos virais sendo compartilhados entre os usuários, através de uma solução de cache proativa. Nesta dissertação, apresentamos uma proposta de cache proativo de conteúdo ciente às interações sociais online para redes com comunicação D2D. Em nossa proposta, determinamos de forma probabilística qual o melhor dispositivo para armazenar um conteúdo que esteja sendo compartilhado entre os usuários com base nas informações de contatos entre os usuários na rede D2D. Os resultados coletados com o simulador ns-3 mostram que a proposta apresentada pode melhorar o descarregamento de dados da rede celular, reduzir os gastos de energia dos dispositivos dos usuários, e fornecer tempos de transmissão de conteúdo menores, em comparação com outro trabalho da literatura,

The astronomical increase in the number of wireless devices such as smart phones in 21th century has revolutionized the way people communicate with one another and share information. The new wireless technologies have also enabled researchers to collect real data about how people move and meet one another in different social settings. Understanding human mobility has many applications in different areas such as traffic planning in cities and public health studies of epidemic diseases. In this thesis, we study the fundamental properties of human contact graphs in order to characterize how people meet one another in different social environments. Understanding human contact patterns in return allows us to propose a cost-effective routing algorithm for spreading information in Delay Tolerant Networks. Furthermore, we propose several contact predictors to predict the unobserved parts of contact graphs when only partial observations are available. Our results show that we are able to infer hidden contacts of real contact traces by exploiting the underlying properties of contact graphs. In the last few years, we have also witnessed an explosion in the number of people who use social media to share information with their friends. In the last part of this thesis, we study the running times of several information spreading algorithms in social networks in order to find the fastest strategy. Fast information spreading has an obvious application in advertising a product to a large number of people in a short amount of time. We prove that a fast information spreading algorithm should efficiently identify communication bottlenecks in order to speed up the running time. Finally, we show that sparsifying large social graphs by exploiting the edge-betweenness centrality measure can also speed up the information spreading rate. / Graduate