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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
View references (71)
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
OWL 2 modeling and reasoning with complex human activities
No full textIn recent years, there has been a growing interest in the adoption of ontologies and ontological reasoning to automatically recognize complex context data such as human activities. In particular, the Web Ontology Language (OWL) emerged as the language of choice, being a standard for the Semantic Web, and supported by a number of tools for knowledge engineering and reasoning. However, the limitations of OWL 1 in terms of expressiveness have been recognized in various fields, and important research efforts have been made to extend the language while preserving decidability of its OWL 1 DL fragment. The result of such work is OWL 2. In this paper we investigate the use of OWL 2 for modeling complex activities and reasoning with them. We show that the new language constructors of OWL 2 overcome the main limitations of OWL 1 for the representation of activities; OWL 2 axioms can be used to represent certain rules and rule-based reasoning previously demanded to hybrid approaches, with the advantage of having a unique semantics, avoiding potential inconsistencies. Then, we propose a system architecture showing the integration of a novel OWL 2 activity ontology and reasoning modules with distributed modules for sensor data aggregation and reasoning. The feasibility of our solution is shown by an extensive experimental evaluation with simulations of different intelligent environments. (C) 2011 Elsevier B.V. All rights reserved
COSAR : hybrid reasoning for context-aware activity recognition
No full textHuman activity recognition is a challenging problem for context-aware systems and applications. Research in this field has mainly adopted techniques based on supervised learning algorithms, but these systems suffer from scalability issues with respect to the number of considered activities and contextual data. In this paper, we propose a solution based on the use of ontologies and ontological reasoning combined with statistical inferencing. Structured symbolic knowledge about the environment surrounding the user allows the recognition system to infer which activities among the candidates identified by statistical methods are more likely to be the actual activity that the user is performing. Ontological reasoning is also integrated with statistical methods to recognize complex activities that cannot be derived by statistical methods alone. The effectiveness of the proposed technique is supported by experiments with a complete implementation of the system using commercially available sensors and an Android-based handheld device as the host for the main activity recognition module
Environmental stress and flowering time: the photoperiodic connection
No full textPlants maximize their chances to survive adversities by reprogramming their development according to environmental conditions. Adaptive variations in the timing to flowering reflect the need for plants to set seeds under the most favorable conditions. A complex network of genetic pathways allows plants to detect and integrate external (e.g., photoperiod and temperature) and/or internal (e.g., age) information to initiate the floral transition. Furthermore different types of environmental stresses play an important role in the floral transition. The emerging picture is that stress conditions often affect flowering through modulation of the photoperiodic pathway. In this review we will discuss different modes of cross talk between stress signaling and photoperiodic flowering, highlighting the central role of the florigen genes in this process.Matteo Riboni, Alice Robustelli Test, Massimo Galbiati, Chiara Tonelli & Lucio
Cont
Constructing the terrorist in the decisions of the supreme court of the United States and the European court of human rights
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Conference Live-Tweeting: the Ultimate Frontier of Knowledge Dissemination?
No full textOver the last decade, the affordances of new portable technological devices such as smartphones have been increasingly converging with those of computers. As a consequence, users have acquired the possibility of accessing the Internet whenever and wherever they wish. Constantly updating one’s status on social networks and taking part in virtual conversations has become a rather common practice. Live-tweeting, i.e engaging on the microblogging platform Twitter for a continuous period of time with a sequence of focused entries (“tweets” of 140 characters) in order to cover an unfolding live event (), is rapidly spreading among many professional categories like scholars and academics. The latter exploit Twitter potentially ubiquitous and real-time communication to report about the conferences they attend, thus keeping their readers informed about what is being discussed and enabling them to virtually attend the conference. It is possible to assume that the receivers of conference tweeting will certainly include scholars, but possibly also lay readers, who bump into it while pursuing other interests. Since live tweets can potentially reach anybody who surfs the Net and there is no way of knowing who actually reads them, the term “networked audience” has been coined to refer to the stratified and interconnected audience of Twitter entries, which ‘consists of real and potential viewers’ of tweets and ‘who are connected not only to the user, but to each other, creating an active, communicative network’ (Marwick and Boyd 2010). So that it can reach such a heterogeneous audience, the information disseminated by presenters and contributors alike undergoes a process of re-contextualization as it is adjusted to fit the tweet format.
In light of these considerations, this paper intends to approach the theme of knowledge dissemination through social networks, and particularly Twitter, addressing the following issues:
1. What is the communicative purpose of conference tweets? Are they meant primarily to fulfill an ideational or a relational function? And depending on this, can they be considered an emerging genre in knowledge dissemination?
2. What type of contents are most commonly disseminated via tweets, i.e. is it more likely to find a synthetic recapitulation of findings or a problematisation of issues? What cognitive processes are involved? This would in turn shed light on the epistemological role attributed to this medium.
3. How do the medium affordances impact on the cognitive and linguistic processes at stake? Are there any recurrent traits characterizing the structure of both single tweets and whole ‘discussions’?
4.
In order to answer these questions, a sample of tweets will be downloaded, including live tweeting (both edited by a moderator and unedited), traditional asynchronous posts taken from conference pages and users’ profiles. Their content will be processed manually as well as with software for corpus interrogation, and whenever possible these materials will be considered alongside the original communicative event from which they originated (conference presentation slides or video recording).
References
Boyd, Danah / Golder, Scott / Lotan, Gilad. 2010. Tweet Tweet Retweet: Conversational Aspects of Retweeting on Twitter. Proceedings of the 43th Annual Hawaii International Conference on System Sciences HICSS-43, IEEE: Kauai, HI. .
Live-Tweeting Best Practices. Twitter.< https://dev.twitter.com/media/live-tweeting.
Context-aware Web services for distributed retrieval of points of interest
No full textDue to the widespread availability of accurate localization technologies, navigation systems are more and more present on mobile devices. These applications usually provide facilities for managing and searching points of interest. However, currently available navigation software does not support sharing of points of interest, and the search facilities are quite primitive, being exclusively based on location and categories. In this paper we present novel algorithms for context-aware retrieval of distributed resources. These algorithms can be executed on any unstructured peer-to-peer network, and are based on the distributed evaluation of a scoring function that takes into account a wide set of context data. The algorithms preserve the correctness of the result set until a certain time-to-live, while reducing the exchange of data in the network. The proposed algorithms have been integrated into a Web service-based, peer-to-peer system for management and sharing of an extended form of points of interest. © 2007 IEEE
Differentially-private release of check-in data for venue recommendation
No full textRecommender systems suggesting venues offer very useful services to people on the move and a great business opportunity for advertisers. These systems suggest venues by matching the current context of the user with the venue features, and consider the popularity of venues, based on the number of visits ('check-ins') that they received. Check-ins may be explicitly communicated by users to geo-social networks, or implicitly derived by analysing 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 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 recommender system providers would like to buy and use to improve their systems, 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 and trusted mobile providers while preserving the utility of check-in information for recommendation purposes. Our technique is based on the use of differential privacy methods integrated with a pre-filtering process, and protects against both an untrusted recommender system and its users, willing to infer the venues and sensitive locations visited by other users. Extensive experiments with a large dataset of real users' check-ins show the effectiveness of our methods. © 2014 IEEE
Context provenance to enhance the dependability of ambient intelligence systems
No full textAmbient intelligence systems would benefit from the possibility of assessing quality and reliability of context information based on its derivation history, named provenance. While various provenance frameworks have been proposed in data management, context data have some peculiar features that claim for a specific support. However, no provenance model specifically targeted to context data has been proposed till the time of writing. In this paper, we report an initial investigation of this challenging research issue by proposing a provenance model for data acquired and processed in ambient intelligence systems. Our model supports representation of complex derivation processes, integrity verification, and a shared ontology to facilitate interoperability. The model also deals with uncertainty and takes into account temporal aspects related to the quality of data. We experimentally show the impact of the provenance model in terms of increased dependability of a sensor-based smart-home infrastructure. We also conducted experiments to evaluate the communication and computational overhead introduced to support our provenance model, using sensors and mobile devices currently available on the market
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