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Context-aware pervasive interfaces
No full textThe proliferation of pervasive services requires advanced methods to adapt the service provision to the user's context. The author presents a hybrid statistical and semantic framework for interface selection and adaptation. The approach is to find the best compromise between urgency and privacy requirements, avoiding interference with the user's activities
Continuous Media Adaptation for Mobile Computing Using Coarse-Grained Asyncronous Notifications
No full textThe recent spreading of public wireless infrastructures allowing for higher data rates makes mobile communications networks a very attractive platform for distribution of multimedia content. At the same time, limited resources in public wireless networks pose serious questions on how to bring services and multimedia to terminals to be used anywhere. Content adaptation is required in order to bring the best perceptual experience to the end-user while optimizing resources usage. Unfortunately, content adaptation is very difficult to achieve and is usually related to bandwidth availability only. In this paper we propose to extend existing service provisioning architectures with an asynchronous notification system to keep up-to-date the whole set of user profile data during service provisioning. We argue that the averagemultimedia application behavior, still adhering to a model based on a very limited number of choices, is not affected by increased reaction time and coarse-grained parameters responsivity. Furthermore, introduction of asynchronous notifications will enable service providers to adapt content considering any parameter characterizing the user profile, not just available bandwidth
Research Challenges for Personal and Collective Awareness
No full textThe "big data" explicitly produced by people through social applications, or implicitly gathered through sensors and transaction records, enables a new generation of mining and analysis tools to understand the trends and dynamics of today's interconnected society. While important steps have been made towards personal, urban, and social awareness, several research challenges still need to be addressed to fully realize the pervasive computing vision. On the one hand, the lack of standard languages and common semantic frameworks strongly limit the possibility to opportunistically acquire available context data, reason with it, and provide proactive services. On the other hand, existing techniques for identifying complex contextual situations are mainly restricted to the recognition of simple actions and activities. Most importantly, due to the unprecedented quantity of digital traces that people leave as they go about their everyday lives, formal privacy methods and trust models must be enforced to avoid the "big data" vision turning into a "big brother" nightmare. In this chapter, the authors discuss the above-mentioned research issues and highlight promising research directions
Privacy Protection in Pervasive Systems : State of the Art and Technical Challenges
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Pervasive and Mobile Computing
Volume 17, Issue PB, 1 February 2015, Pages 159-174
Privacy protection in pervasive systems: State of the art and technical challenges (Article)
Bettini, C. ,
Riboni, D.
Università Degli Studi di Milano, D.I., via Comelico 39, Milan, Italy
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Abstract
Pervasive and mobile computing applications are dramatically increasing the amount of personal data released to service providers as well as to third parties. Data includes geographical and indoor positions of individuals, their movement patterns as well as sensor-acquired data that may reveal individuals' physical conditions, habits, and, in general, information that may lead to undesired consequences like unsolicited advertisement or more serious ones like discrimination and stalking. In this survey paper, at first we consider representative classes of pervasive applications, and identify the requirements they impose in terms of privacy and trade-off with service quality. Then, we review the most prominent privacy preservation approaches, we discuss and summarize them in terms of the requirements. Finally, we take a more holistic view of the privacy problem by discussing other aspects that turn out to be crucial for the widespread adoption of privacy enhancing technologies. We discuss technical challenges like the need for tools augmenting the awareness of individuals and to capture their privacy preferences, as well as legal and economic challenges. Indeed, on one side privacy solutions must comply to ethical and legal requirements, and not prevent profitable business models, while on the other side it is unlikely that privacy preserving solutions will become practical and effective without new regulations
Incremental release of differentially-private check-in data
No full textDue to the growing popularity of location-based services and geo-social networks, users communicate more and more private location traces to service providers, as well as explicit spatio-temporal data, often called "check-ins", about their presence in specific venues at given times. Further check-in data may be implicitly derived by analyzing location data collected by mobile services. In general, the visibility of explicit check-ins is limited to friends in the social network, while the visibility of implicit check-ins is limited to the service provider. Exposing check-ins to unauthorized users is a privacy threat since recurring presence in given locations may reveal political opinions, religious beliefs, or sexual orientation, as well as absence from other locations where the user is supposed to be. Hence, on one side mobile app providers host valuable information that they would like to sell to possibly untrusted third parties, and on the other we recognize serious privacy issues in releasing that information. In this paper, we solve this dilemma by providing formal privacy guarantees to users, while preserving the utility of check-in data. Our technique is based on the use of differential privacy methods integrated with a pre-filtering process, and protects both against an untrusted third party receiving check-in statistics, and against its users, willing to infer the venues and sensitive locations visited by other users. We show how the technique can be extended to support incremental releases of check-in data. Extensive experiments with a large dataset of real users' check-ins show the effectiveness of our methods
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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