Socially-Aware Distributed Hash Tables for Decentralized Online Social Networks

Many decentralized online social networks (DOSNs) have been proposed due to an increase in awareness related to privacy and scalability issues in centralized social networks. Such decentralized networks transfer processing and storage functionalities from the service providers towards the end users. DOSNs require individualistic implementation for services, (i.e., search, information dissemination, storage, and publish/subscribe). However, many of these services mostly perform social queries, where OSN users are interested in accessing information of their friends. In our work, we design a socially-aware distributed hash table (DHTs) for efficient implementation of DOSNs. In particular, we propose a gossip-based algorithm to place users in a DHT, while maximizing the social awareness among them. Through a set of experiments, we show that our approach reduces the lookup latency by almost 30% and improves the reliability of the communication by nearly 10% via trusted contacts.Comment: 10 pages, p2p 2015 conferenc

Systematizing Decentralization and Privacy: Lessons from 15 Years of Research and Deployments

Decentralized systems are a subset of distributed systems where multiple authorities control different components and no authority is fully trusted by all. This implies that any component in a decentralized system is potentially adversarial. We revise fifteen years of research on decentralization and privacy, and provide an overview of key systems, as well as key insights for designers of future systems. We show that decentralized designs can enhance privacy, integrity, and availability but also require careful trade-offs in terms of system complexity, properties provided, and degree of decentralization. These trade-offs need to be understood and navigated by designers. We argue that a combination of insights from cryptography, distributed systems, and mechanism design, aligned with the development of adequate incentives, are necessary to build scalable and successful privacy-preserving decentralized systems

Pretty Private Group Management

Group management is a fundamental building block of today's Internet applications. Mailing lists, chat systems, collaborative document edition but also online social networks such as Facebook and Twitter use group management systems. In many cases, group security is required in the sense that access to data is restricted to group members only. Some applications also require privacy by keeping group members anonymous and unlinkable. Group management systems routinely rely on a central authority that manages and controls the infrastructure and data of the system. Personal user data related to groups then becomes de facto accessible to the central authority. In this paper, we propose a completely distributed approach for group management based on distributed hash tables. As there is no enrollment to a central authority, the created groups can be leveraged by various applications. Following this paradigm we describe a protocol for such a system. We consider security and privacy issues inherently introduced by removing the central authority and provide a formal validation of security properties of the system using AVISPA. We demonstrate the feasibility of this protocol by implementing a prototype running on top of Vuze's DHT

Clustering algorithm for D2D communication in next generation cellular networks : thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Engineering, Massey University, Auckland, New Zealand

Next generation cellular networks will support many complex services for smartphones, vehicles, and other devices. To accommodate such services, cellular networks need to go beyond the capabilities of their previous generations. Device-to-Device communication (D2D) is a key technology that can help fulfil some of the requirements of future networks. The telecommunication industry expects a significant increase in the density of mobile devices which puts more pressure on centralized schemes and poses risk in terms of outages, poor spectral efficiencies, and low data rates. Recent studies have shown that a large part of the cellular traffic pertains to sharing popular contents. This highlights the need for decentralized and distributive approaches to managing multimedia traffic. Content-sharing via D2D clustered networks has emerged as a popular approach for alleviating the burden on the cellular network. Different studies have established that D2D communication in clusters can improve spectral and energy efficiency, achieve low latency while increasing the capacity of the network. To achieve effective content-sharing among users, appropriate clustering strategies are required. Therefore, the aim is to design and compare clustering approaches for D2D communication targeting content-sharing applications. Currently, most of researched and implemented clustering schemes are centralized or predominantly dependent on Evolved Node B (eNB). This thesis proposes a distributed architecture that supports clustering approaches to incorporate multimedia traffic. A content-sharing network is presented where some D2D User Equipment (DUE) function as content distributors for nearby devices. Two promising techniques are utilized, namely, Content-Centric Networking and Network Virtualization, to propose a distributed architecture, that supports efficient content delivery. We propose to use clustering at the user level for content-distribution. A weighted multi-factor clustering algorithm is proposed for grouping the DUEs sharing a common interest. Various performance parameters such as energy consumption, area spectral efficiency, and throughput have been considered for evaluating the proposed algorithm. The effect of number of clusters on the performance parameters is also discussed. The proposed algorithm has been further modified to allow for a trade-off between fairness and other performance parameters. A comprehensive simulation study is presented that demonstrates that the proposed clustering algorithm is more flexible and outperforms several well-known and state-of-the-art algorithms. The clustering process is subsequently evaluated from an individual user’s perspective for further performance improvement. We believe that some users, sharing common interests, are better off with the eNB rather than being in the clusters. We utilize machine learning algorithms namely, Deep Neural Network, Random Forest, and Support Vector Machine, to identify the users that are better served by the eNB and form clusters for the rest of the users. This proposed user segregation scheme can be used in conjunction with most clustering algorithms including the proposed multi-factor scheme. A comprehensive simulation study demonstrates that with such novel user segregation, the performance of individual users, as well as the whole network, can be significantly improved for throughput, energy consumption, and fairness

Agents come to bits: Towards a constructive comprehensive taxonomy of economic entities

Mirowski [Mirowski, P., 2007. Markets come to bits: evolution, computation, and markomata in economic science. Journal of Economic Behavior and Organization 63, 209–242] argues for a constructive approach to economic modeling centered on markets as evolving computational entities. This essay counters that a broader constructive approach to economic modeling can and should be taken. The recent advent of powerful computer technologies supporting agent-based modeling (ABM) renders feasible the computational study of economies modeled as evolving systems of interacting agents. In ABM, an “agent” refers broadly to bundled data and behavioral methods representing an entity constituting part of a computationally constructed world. Examples of possible agent referents include individuals, social groupings, institutions (e.g., markets), biological entities such as crops, and physical entities such as transportation networks and weather. Consequently, ABM provides tremendous opportunities for economists and other social scientists to tailor the breadth and depth of the entities represented in their models to the application at hand. A simple ABM of a two-sector decentralized market economy is used for concrete illustration

Doctor of Philosophy

dissertationWe develop a novel framework for friend-to-friend (f2f) distributed services (F3DS) by which applications can easily offer peer-to-peer (p2p) services among social peers with resource sharing governed by approximated levels of social altruism. Our frame- work differs significantly from typical p2p collaboration in that it provides a founda- tion for distributed applications to cooperate based on pre-existing trust and altruism among social peers. With the goal of facilitating the approximation of relative levels of altruism among social peers within F3DS, we introduce a new metric: SocialDistance. SocialDistance is a synthetic metric that combines direct levels of altruism between peers with an altruism decay for each hop to approximate indirect levels of altruism. The resulting multihop altruism levels are used by F3DS applications to proportion and prioritize the sharing of resources with other social peers. We use SocialDistance to implement a novel flash file/patch distribution method, SocialSwarm. SocialSwarm uses the SocialDistance metric as part of its resource allocation to overcome the neces- sity of (and inefficiency created by) resource bartering among friends participating in a BitTorrent swarm. We find that SocialSwarm achieves an average file download time reduction of 25% to 35% in comparison with standard BitTorrent under a variety of configurations and conditions, including file sizes, maximum SocialDistance, as well as leech and seed counts. The most socially connected peers yield up to a 47% decrease in download completion time in comparison with average nonsocial BitTorrent swarms. We also use the F3DS framework to implement novel malware detection application- F3DS Antivirus (F3AV)-and evaluate it on the Amazon cloud. We show that with f2f sharing of resources, F3AV achieves a 65% increase in the detection rate of 0- to 1-day-old malware among social peers as compared to the average of individual scanners. Furthermore, we show that F3AV provides the greatest diversity of mal- ware scanners (and thus malware protection) to social hubs-those nodes that are positioned to provide strategic defense against socially aware malware

Agent-Based Computational Economics: A Constructive Approach to Economic Theory

This chapter explores the potential advantages and disadvantages of Agent-based Computational Economics (ACE) for the study of economic systems. General points are concretely illustrated using an ACE model of a two-sector decentralized market economy. Six issues are highlighted: Constructive understanding of production, pricing, and trade processes; the essential primacy of survival; strategic rivalry and market power; behavioral uncertainty and learning; the role of conventions and organizations; and the complex interactions among structural attributes, behaviors, and institutional arrangements. Extensive annotated pointers to ACE surveys, research, course materials, and software can be accessed here: http://www.econ.iastate.edu/tesfatsi/ace.htmagent-based computational economics; Learning; network formation; decentralized market economy

Context-aware collaborative storage and programming for mobile users

Since people generate and access most digital content from mobile devices, novel innovative mobile apps and services are possible. Most people are interested in sharing this content with communities defined by friendship, similar interests, or geography in exchange for valuable services from these innovative apps. At the same time, they want to own and control their content. Collaborative mobile computing is an ideal choice for this situation. However, due to the distributed nature of this computing environment and the limited resources on mobile devices, maintaining content availability and storage fairness as well as providing efficient programming frameworks are challenging. This dissertation explores several techniques to improve these shortcomings of collaborative mobile computing platforms. First, it proposes a medley of three techniques into one system, MobiStore, that offers content availability in mobile peer-to-peer networks: topology maintenance with robust connectivity, structural reorientation based on the current state of the network, and gossip-based hierarchical updates. Experimental results showed that MobiStore outperforms a state-of-the-art comparison system in terms of content availability and resource usage fairness. Next, the dissertation explores the usage of social relationship properties (i.e., network centrality) to improve the fairness of resource allocation for collaborative computing in peer-to-peer online social networks. The challenge is how to provide fairness in content replication for P2P-OSN, given that the peers in these networks exchange information only with one-hop neighbors. The proposed solution provides fairness by selecting the peers to replicate content based on their potential to introduce the storage skewness, which is determined from their structural properties in the network. The proposed solution, Philia, achieves higher content availability and storage fairness than several comparison systems. The dissertation concludes with a high-level distributed programming model, which efficiently uses computing resources on a cloud-assisted, collaborative mobile computing platform. This platform pairs mobile devices with virtual machines (VMs) in the cloud for increased execution performance and availability. On such a platform, two important challenges arise: first, pairing the two computing entities into a seamless computation, communication, and storage unit; and second, using the computing resources in a cost-effective way. This dissertation proposes Moitree, a distributed programming model and middleware that translates high-level programming constructs into events and provides the illusion of a single computing entity over the mobile-VM pairs. From programmers’ viewpoint, the Moitree API models user collaborations into dynamic groups formed over location, time, or social hierarchies. Experimental results from a prototype implementation show that Moitree is scalable, suitable for real-time apps, and can improve the performance of collaborating apps regarding latency and energy consumption