11,842 research outputs found

    Confidentiality-Preserving Publish/Subscribe: A Survey

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    Publish/subscribe (pub/sub) is an attractive communication paradigm for large-scale distributed applications running across multiple administrative domains. Pub/sub allows event-based information dissemination based on constraints on the nature of the data rather than on pre-established communication channels. It is a natural fit for deployment in untrusted environments such as public clouds linking applications across multiple sites. However, pub/sub in untrusted environments lead to major confidentiality concerns stemming from the content-centric nature of the communications. This survey classifies and analyzes different approaches to confidentiality preservation for pub/sub, from applications of trust and access control models to novel encryption techniques. It provides an overview of the current challenges posed by confidentiality concerns and points to future research directions in this promising field

    The OCarePlatform : a context-aware system to support independent living

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    Background: Currently, healthcare services, such as institutional care facilities, are burdened with an increasing number of elderly people and individuals with chronic illnesses and a decreasing number of competent caregivers. Objectives: To relieve the burden on healthcare services, independent living at home could be facilitated, by offering individuals and their (in)formal caregivers support in their daily care and needs. With the rise of pervasive healthcare, new information technology solutions can assist elderly people ("residents") and their caregivers to allow residents to live independently for as long as possible. Methods: To this end, the OCarePlatform system was designed. This semantic, data-driven and cloud based back-end system facilitates independent living by offering information and knowledge-based services to the resident and his/her (in)formal caregivers. Data and context information are gathered to realize context-aware and personalized services and to support residents in meeting their daily needs. This body of data, originating from heterogeneous data and information sources, is sent to personalized services, where is fused, thus creating an overview of the resident's current situation. Results: The architecture of the OCarePlatform is proposed, which is based on a service-oriented approach, together with its different components and their interactions. The implementation details are presented, together with a running example. A scalability and performance study of the OCarePlatform was performed. The results indicate that the OCarePlatform is able to support a realistic working environment and respond to a trigger in less than 5 seconds. The system is highly dependent on the allocated memory. Conclusion: The data-driven character of the OCarePlatform facilitates easy plug-in of new functionality, enabling the design of personalized, context-aware services. The OCarePlatform leads to better support for elderly people and individuals with chronic illnesses, who live independently. (C) 2016 Elsevier Ireland Ltd. All rights reserved

    Malicious entities are in vain : preserving privacy in publish and subscribe systems

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    Publish and subscribe (pub/sub) system is a decoupled communication paradigm that allows routing of publications. Through a set of dedicated third party servers, referred to as brokers, publications are disseminated without establishing any link between publishers and subscribers. However, the involvement of these brokers raises security and privacy issues as they can harvest sensitive data about subscribers. Furthermore, a malicious broker may collude with malicious subscribers and/or publishers to infer subscribers’ interests. Our solution is such that subscribers’ interests are not revealed to curious brokers and published data can only be accessed by the authorised subscribers. Moreover, the proposed protocol is secure against the collusion attacks between malicious brokers, publishers, and subscribers

    Ethical Challenges in Data-Driven Dialogue Systems

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    The use of dialogue systems as a medium for human-machine interaction is an increasingly prevalent paradigm. A growing number of dialogue systems use conversation strategies that are learned from large datasets. There are well documented instances where interactions with these system have resulted in biased or even offensive conversations due to the data-driven training process. Here, we highlight potential ethical issues that arise in dialogue systems research, including: implicit biases in data-driven systems, the rise of adversarial examples, potential sources of privacy violations, safety concerns, special considerations for reinforcement learning systems, and reproducibility concerns. We also suggest areas stemming from these issues that deserve further investigation. Through this initial survey, we hope to spur research leading to robust, safe, and ethically sound dialogue systems.Comment: In Submission to the AAAI/ACM conference on Artificial Intelligence, Ethics, and Societ

    THREE TEMPORAL PERSPECTIVES ON DECENTRALIZED LOCATION-AWARE COMPUTING: PAST, PRESENT, FUTURE

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    Durant les quatre derniĂšres dĂ©cennies, la miniaturisation a permis la diffusion Ă  large Ă©chelle des ordinateurs, les rendant omniprĂ©sents. Aujourd’hui, le nombre d’objets connectĂ©s Ă  Internet ne cesse de croitre et cette tendance n’a pas l’air de ralentir. Ces objets, qui peuvent ĂȘtre des tĂ©lĂ©phones mobiles, des vĂ©hicules ou des senseurs, gĂ©nĂšrent de trĂšs grands volumes de donnĂ©es qui sont presque toujours associĂ©s Ă  un contexte spatiotemporel. Le volume de ces donnĂ©es est souvent si grand que leur traitement requiert la crĂ©ation de systĂšme distribuĂ©s qui impliquent la coopĂ©ration de plusieurs ordinateurs. La capacitĂ© de traiter ces donnĂ©es revĂȘt une importance sociĂ©tale. Par exemple: les donnĂ©es collectĂ©es lors de trajets en voiture permettent aujourd’hui d’éviter les em-bouteillages ou de partager son vĂ©hicule. Un autre exemple: dans un avenir proche, les donnĂ©es collectĂ©es Ă  l’aide de gyroscopes capables de dĂ©tecter les trous dans la chaussĂ©e permettront de mieux planifier les interventions de maintenance Ă  effectuer sur le rĂ©seau routier. Les domaines d’applications sont par consĂ©quent nombreux, de mĂȘme que les problĂšmes qui y sont associĂ©s. Les articles qui composent cette thĂšse traitent de systĂšmes qui partagent deux caractĂ©ristiques clĂ©s: un contexte spatiotemporel et une architecture dĂ©centralisĂ©e. De plus, les systĂšmes dĂ©crits dans ces articles s’articulent autours de trois axes temporels: le prĂ©sent, le passĂ©, et le futur. Les systĂšmes axĂ©s sur le prĂ©sent permettent Ă  un trĂšs grand nombre d’objets connectĂ©s de communiquer en fonction d’un contexte spatial avec des temps de rĂ©ponses proche du temps rĂ©el. Nos contributions dans ce domaine permettent Ă  ce type de systĂšme dĂ©centralisĂ© de s’adapter au volume de donnĂ©e Ă  traiter en s’étendant sur du matĂ©riel bon marchĂ©. Les systĂšmes axĂ©s sur le passĂ© ont pour but de faciliter l’accĂšs a de trĂšs grands volumes donnĂ©es spatiotemporelles collectĂ©es par des objets connectĂ©s. En d’autres termes, il s’agit d’indexer des trajectoires et d’exploiter ces indexes. Nos contributions dans ce domaine permettent de traiter des jeux de trajectoires particuliĂšrement denses, ce qui n’avait pas Ă©tĂ© fait auparavant. Enfin, les systĂšmes axĂ©s sur le futur utilisent les trajectoires passĂ©es pour prĂ©dire les trajectoires que des objets connectĂ©s suivront dans l’avenir. Nos contributions permettent de prĂ©dire les trajectoires suivies par des objets connectĂ©s avec une granularitĂ© jusque lĂ  inĂ©galĂ©e. Bien qu’impliquant des domaines diffĂ©rents, ces contributions s’articulent autour de dĂ©nominateurs communs des systĂšmes sous-jacents, ouvrant la possibilitĂ© de pouvoir traiter ces problĂšmes avec plus de gĂ©nĂ©ricitĂ© dans un avenir proche. -- During the past four decades, due to miniaturization computing devices have become ubiquitous and pervasive. Today, the number of objects connected to the Internet is in- creasing at a rapid pace and this trend does not seem to be slowing down. These objects, which can be smartphones, vehicles, or any kind of sensors, generate large amounts of data that are almost always associated with a spatio-temporal context. The amount of this data is often so large that their processing requires the creation of a distributed system, which involves the cooperation of several computers. The ability to process these data is important for society. For example: the data collected during car journeys already makes it possible to avoid traffic jams or to know about the need to organize a carpool. Another example: in the near future, the maintenance interventions to be carried out on the road network will be planned with data collected using gyroscopes that detect potholes. The application domains are therefore numerous, as are the prob- lems associated with them. The articles that make up this thesis deal with systems that share two key characteristics: a spatio-temporal context and a decentralized architec- ture. In addition, the systems described in these articles revolve around three temporal perspectives: the present, the past, and the future. Systems associated with the present perspective enable a very large number of connected objects to communicate in near real-time, according to a spatial context. Our contributions in this area enable this type of decentralized system to be scaled-out on commodity hardware, i.e., to adapt as the volume of data that arrives in the system increases. Systems associated with the past perspective, often referred to as trajectory indexes, are intended for the access to the large volume of spatio-temporal data collected by connected objects. Our contributions in this area makes it possible to handle particularly dense trajectory datasets, a problem that has not been addressed previously. Finally, systems associated with the future per- spective rely on past trajectories to predict the trajectories that the connected objects will follow. Our contributions predict the trajectories followed by connected objects with a previously unmet granularity. Although involving different domains, these con- tributions are structured around the common denominators of the underlying systems, which opens the possibility of being able to deal with these problems more generically in the near future

    Collusion defender : preserving subscribers’ privacy in publish and subscribe systems

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    The Publish and Subscribe (pub/sub) system is an established paradigm to disseminate the data from publishers to subscribers in a loosely coupled manner using a network of dedicated brokers. However, sensitive data could be exposed to malicious entities if brokers get compromised or hacked; or even worse, if brokers themselves are curious to learn about the data. A viable mechanism to protect sensitive publications and subscriptions is to encrypt the data before it is disseminated through the brokers. State-of-the-art approaches allow brokers to perform encrypted matching without revealing publications and subscriptions. However, if malicious brokers collude with malicious subscribers or publishers, they can learn the interests of innocent subscribers, even when the interests are encrypted. In this article, we present a pub/sub system that ensures confidentiality of publications and subscriptions in the presence of untrusted brokers. Furthermore, our solution resists collusion attacks between untrusted brokers and malicious subscribers (or publishers). Finally, we have implemented a prototype of our solution to show its feasibility and efficiency. Index Terms: Collusion Resistance, Secure Pub/sub, Subscribers’ Privacy, Publications’ Confidentialit

    Tag-Aware Recommender Systems: A State-of-the-art Survey

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    In the past decade, Social Tagging Systems have attracted increasing attention from both physical and computer science communities. Besides the underlying structure and dynamics of tagging systems, many efforts have been addressed to unify tagging information to reveal user behaviors and preferences, extract the latent semantic relations among items, make recommendations, and so on. Specifically, this article summarizes recent progress about tag-aware recommender systems, emphasizing on the contributions from three mainstream perspectives and approaches: network-based methods, tensor-based methods, and the topic-based methods. Finally, we outline some other tag-related works and future challenges of tag-aware recommendation algorithms.Comment: 19 pages, 3 figure
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