2,914 research outputs found

    Technical considerations towards mobile user QoE enhancement via Cloud interaction

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    This paper discusses technical considerations of a Cloud infrastructure which interacts with mobile devices in order to migrate part of the computational overhead from the mobile device to the Cloud. The aim of the interaction between the mobile device and the Cloud is the enhancement of parameters that affect the Quality of Experience (QoE) of the mobile end user through the offloading of computational aspects of demanding applications. This paper shows that mobile user’s QoE can be potentially enhanced by offloading computational tasks to the Cloud which incorporates a predictive context-aware mechanism to schedule delivery of content to the mobile end-user using a low-cost interaction model between the Cloud and the mobile user. With respect to the proposed enhancements, both the technical considerations of the cloud infrastructure are examined, as well as the interaction between the mobile device and the Cloud

    Reuse It Or Lose It: More Efficient Secure Computation Through Reuse of Encrypted Values

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    Two-party secure function evaluation (SFE) has become significantly more feasible, even on resource-constrained devices, because of advances in server-aided computation systems. However, there are still bottlenecks, particularly in the input validation stage of a computation. Moreover, SFE research has not yet devoted sufficient attention to the important problem of retaining state after a computation has been performed so that expensive processing does not have to be repeated if a similar computation is done again. This paper presents PartialGC, an SFE system that allows the reuse of encrypted values generated during a garbled-circuit computation. We show that using PartialGC can reduce computation time by as much as 96% and bandwidth by as much as 98% in comparison with previous outsourcing schemes for secure computation. We demonstrate the feasibility of our approach with two sets of experiments, one in which the garbled circuit is evaluated on a mobile device and one in which it is evaluated on a server. We also use PartialGC to build a privacy-preserving "friend finder" application for Android. The reuse of previous inputs to allow stateful evaluation represents a new way of looking at SFE and further reduces computational barriers.Comment: 20 pages, shorter conference version published in Proceedings of the 2014 ACM SIGSAC Conference on Computer and Communications Security, Pages 582-596, ACM New York, NY, US

    Networking Group Content: RESTful Multiparty Access to a Data-centric Web of Things

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    Content replication to many destinations is a common use case in the Internet of Things (IoT). The deployment of IP multicast has proven inefficient, though, due to its lack of layer-2 support by common IoT radio technologies and its synchronous end-to-end transmission, which is highly susceptible to interference. Information-centric networking (ICN) introduced hop-wise multi-party dissemination of cacheable content, which has proven valuable in particular for low-power lossy networking regimes. Even NDN, however, the most prominent ICN protocol, suffers from a lack of deployment. In this paper, we explore how multiparty content distribution in an information-centric Web of Things (WoT) can be built on CoAP. We augment the CoAP proxy by request aggregation and response replication functions, which together with proxy caches enable asynchronous group communication. In a further step, we integrate content object security with OSCORE into the CoAP multicast proxy system, which enables ubiquitous caching of certified authentic content. In our evaluation, we compare NDN with different deployment models of CoAP, including our data-centric approach in realistic testbed experiments. Our findings indicate that multiparty content distribution based on CoAP proxies performs equally well as NDN, while remaining fully compatible with the established IoT protocol world of CoAP on the Internet

    Security for the Industrial IoT: The Case for Information-Centric Networking

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    Industrial production plants traditionally include sensors for monitoring or documenting processes, and actuators for enabling corrective actions in cases of misconfigurations, failures, or dangerous events. With the advent of the IoT, embedded controllers link these `things' to local networks that often are of low power wireless kind, and are interconnected via gateways to some cloud from the global Internet. Inter-networked sensors and actuators in the industrial IoT form a critical subsystem while frequently operating under harsh conditions. It is currently under debate how to approach inter-networking of critical industrial components in a safe and secure manner. In this paper, we analyze the potentials of ICN for providing a secure and robust networking solution for constrained controllers in industrial safety systems. We showcase hazardous gas sensing in widespread industrial environments, such as refineries, and compare with IP-based approaches such as CoAP and MQTT. Our findings indicate that the content-centric security model, as well as enhanced DoS resistance are important arguments for deploying Information Centric Networking in a safety-critical industrial IoT. Evaluation of the crypto efforts on the RIOT operating system for content security reveal its feasibility for common deployment scenarios.Comment: To be published at IEEE WF-IoT 201

    UFace: Your universal password no one can see

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    With the advantage of not having to memorize long passwords, facial authentication has become a topic of interest among researchers. However, since many users store images containing their face on social networking sites, a new challenge emerges in preventing attackers from impersonating these users by using these online photos. Another problem with most current facial authentication protocols is that they require an unencrypted image of each registered user\u27s face to compare against. Moreover, they might require the user\u27s device to execute computationally expensive multiparty protocols which presents a problem for mobile devices with limited processing power. Finally, these authentication protocols will not be able to be implemented in real systems because they take too long to execute. In this paper, we present a novel privacy preserving facial authentication system, called UFace. Not only does UFace limit the amount of computation for a user\u27s mobile device, but it also prevents unencrypted images from leaving a user\u27 possession while finishing the authentication protocol within seconds. Web services can now outsource their authentication protocol to UFace so that each web service only needs to handle its own functionality. UFace guarantees that it can correctly authenticate each user with 90% accuracy, prevent attacks from using online photos and that all data used in the authentication protocol is done on encrypted randomized data. In other words, only the user can see the facial image and feature vector used for authentication; all other parties execute the protocol using seemingly random information. UFace was implemented through two facets: a mobile client application to obtain and encrypt the feature vector of each user\u27s facial image, and a server protocol to securely authenticate a feature vector using secure multiparty computations. The experimental results demonstrate that UFace can be used as a third party authentication tool for any number of web services --Abstract, page iii

    Crossing Roads of Federated Learning and Smart Grids: Overview, Challenges, and Perspectives

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    Consumer's privacy is a main concern in Smart Grids (SGs) due to the sensitivity of energy data, particularly when used to train machine learning models for different services. These data-driven models often require huge amounts of data to achieve acceptable performance leading in most cases to risks of privacy leakage. By pushing the training to the edge, Federated Learning (FL) offers a good compromise between privacy preservation and the predictive performance of these models. The current paper presents an overview of FL applications in SGs while discussing their advantages and drawbacks, mainly in load forecasting, electric vehicles, fault diagnoses, load disaggregation and renewable energies. In addition, an analysis of main design trends and possible taxonomies is provided considering data partitioning, the communication topology, and security mechanisms. Towards the end, an overview of main challenges facing this technology and potential future directions is presented
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