409,236 research outputs found

    Distributed Social Network - data security

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    The present day Internet provides a wide range of services users can benefit from. Some of the services require gathering, processing and presenting data that come from many users in order to deliver additional information. The suitable example can be social networking service. The more valuable data it stores and processes, the more profitable it can become. Users’ personal data can constitute significant value. One of the issues of social networking is storing and processing data by only one entity. Users cannot choose the most suitable security policy because there is only one provided for certain social network. Being part of it, means accepting the risk of unauthorized data distribution and data leakage because of application vulnerabilities. This paper presents new architecture of social network, which provides mechanisms for dividing data between more than one entity and combining independent data repositories in order to deliver one social network with clearly defined interfaces used to connect new data sources

    Social learning against data falsification in sensor networks

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    Sensor networks generate large amounts of geographically-distributed data. The conventional approach to exploit this data is to first gather it in a special node that then performs processing and inference. However, what happens if this node is destroyed, or even worst, if it is hijacked? To explore this problem, in this work we consider a smart attacker who can take control of critical nodes within the network and use them to inject false information. In order to face this critical security thread, we propose a novel scheme that enables data aggregation and decision-making over networks based on social learning, where the sensor nodes act resembling how agents make decisions in social networks. Our results suggest that social learning enables high network resilience, even when a significant portion of the nodes have been compromised by the attacker

    From Causal History to Social Network in Distributed Social Semantic Software

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    International audienceWeb 2.0 raises the importance of collaboration powered by social software. Social software clearly illustrated how it is possible to convert a community of strangers into a community of collaborators producing all together valuable content. However, collaboration is currently supported by collaboration providers such as Google, Yahoo, etc. following "Collaboration as a Service (CaaS)" approach. This approach arises privacy and censorship issues. Users have to trust CaaS providers for both security of hosted data and usage of collected data. Alternative approaches including private peer-to-peer networks, friend-to-friend networks, distributed version control systems, distributed peer-to-peer groupware, support collaboration without requiring a collaboration provider. Collaboration is powered with the resources provided by the users. If it is easy for a collaboration provider to extract the complete social network graph from the observed interactions. Obtaining social network informations in the distributed approach is more challenging. In fact, the distributed approach is designed to protect privacy of users and thus makes extracting the whole social network difficult. In this paper, we show how it is possible to compute a local view of the social network on each site in a distributed collaborative system approach

    Enhanced cyberspace defense with real-time distributed systems using covert channel publish-subscribe broker pattern communications

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    In this thesis, we propose a novel cyberspace defense solution to the growing sophistication of threats facing networks within the Department of Defense. Current network defense strategies, including traditional intrusion detection and firewall-based perimeter defenses, are ineffective against increasingly sophisticated social engineering attacks such as spear-phishing which exploit individuals with targeted information. These asymmetric attacks are able to bypass current network defense technologies allowing adversaries extended and often unrestricted access to portions of the enterprise. Network defense strategies are hampered by solutions favoring network-centric designs which disregard the security requirements of the specific data and information on the networks. Our solution leverages specific technology characteristics from traditional network defense systems and real-time distributed systems using publish-subscribe broker patterns to form the foundation of a full-spectrum cyber operations capability. Building on this foundation, we present the addition of covert channel communications within the distributed systems framework to protect sensitive Command and Control and Battle Management messaging from adversary intercept and exploitation. Through this combined approach, DoD and Service network defense professionals will be able to meet sophisticated cyberspace threats head-on while simultaneously protecting the data and information critical to warfighting Commands, Services and Agencies.http://archive.org/details/enhancedcyberspa109454049US Air Force (USAF) author.Approved for public release; distribution is unlimited

    Group-Level Frameworks for Data Ethics, Privacy, Safety and Security in Digital Environments

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    In today\u27s digital age, the widespread collection, utilization, and sharing of personal data are challenging our conventional beliefs about privacy and information security. This thesis will explore the boundaries of conventional privacy and security frameworks and investigate new methods to handle online privacy by integrating groups. Additionally, we will examine approaches to monitoring the types of information gathered on individuals to tackle transparency concerns in the data broker and data processor sector. We aim to challenge traditional notions of privacy and security to encourage innovative strategies for safeguarding them in our interconnected, dispersed digital environment. This thesis uses a multi-disciplinary approach to complex systems, drawing from various fields such as data ethics, legal theory, and philosophy. Our methods include complex systems modeling, network analysis, data science, and statistics. As a first step, we investigate the limits of individual consent frameworks in online social media platforms. We develop new security settings, called distributed consent, that can be used in an online social network or coordinated across online platforms. We then model the levels of observability of individuals on the platform(s) to measure the effectiveness of the new security settings against surveillance from third parties. Distributed consent can help to protect individuals online from surveillance, but it requires a high coordination cost on the part of the individual. Users must also decide whether to protect their privacy from third parties and network neighbors by disclosing security settings or taking on the burden of coordinating security on single and multiple platforms. However, the coordination burden may be more appropriate for systems-level regulation. We then explore how groups of individuals can work together to protect themselves from the harms of misinformation on online social networks. Social media users are not equally susceptible to all types of misinformation. Further, diverse groups of social media communities can help protect one another from misinformation by correcting each other\u27s blind spots. We highlight the importance of group diversity in network dynamics and explore how natural diversity within groups can provide protection rather than relying on new technologies such as distributed consent settings. Finally, we investigate methods to interrogate what types of personal data are collected by third parties and measure the risks and harms associated with aggregating personal data. We introduce methods that provide transparency into how modern data collection practices pose risks to data subjects online. We hope that the collection of these results provides a humble step toward revealing gaps in privacy and security frameworks and promoting new solutions for the digital age

    Towards inferring communication patterns in online social networks

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    Grup de recerca: Security of Networks and Distributed Applications (SENDA)The separation between the public and private spheres on online social networks is known to be, at best, blurred. On the one hand, previous studies have shown how it is possible to infer private attributes from publicly available data. On the other hand, no distinction exists between public and private data when we consider the ability of the online social network (OSN) provider to access them. Even when OSN users go to great lengths to protect their privacy, such as by using encryption or communication obfuscation, correlations between data may render these solutions useless. In this article, we study the relationship between private communication patterns and publicly available OSN data. Such a relationship informs both privacy-invasive inferences as well as OSN communication modelling, the latter being key toward developing effective obfuscation tools. We propose an inference model based on Bayesian analysis and evaluate, using a real social network dataset, how archetypal social graph features can lead to inferences about private communication. Our results indicate that both friendship graph and public traffic data may not be informative enough to enable these inferences, with time analysis having a non-negligible impact on their precision
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