112 research outputs found
Privacy-preserving targeted advertising scheme for IPTV using the cloud
In this paper, we present a privacy-preserving scheme for targeted advertising via the Internet Protocol TV (IPTV). The scheme uses a communication model involving a collection of viewers/subscribers, a content provider (IPTV), an advertiser, and a cloud server. To provide high quality directed advertising service, the advertiser can utilize not only demographic information of subscribers, but also their watching habits. The latter includes watching history, preferences for IPTV content and watching rate, which are published on the cloud server periodically (e.g. weekly) along with anonymized demographics. Since the published data may leak sensitive information about subscribers, it is safeguarded using cryptographic techniques in addition to the anonymization of demographics. The techniques used by the advertiser, which can be manifested in its queries to the cloud, are considered (trade) secrets and therefore are protected as well. The cloud is oblivious to the published data, the queries of the advertiser as well as its own responses to these queries. Only a legitimate advertiser, endorsed with a so-called {\em trapdoor} by the IPTV, can query the cloud and utilize the query results. The performance of the proposed scheme is evaluated with experiments, which show that the scheme is suitable for practical usage
Privacy-preserving targeted advertising scheme for IPTV using the cloud
Targeted advertising is an emerging business area that provides effective services for advertisers and end users. Advertising agencies need information about users to send them targeted advertisements. However, uncontrolled access to users' sensitive information for advertising purposes violates the privacy of individuals. Therefore, efficient techniques must be used to not only preserve users privacy but also enable advertisers to reach right users. In this thesis, we present a privacy-preserving scheme for targeted advertising via the Internet Protocol TV (IPTV). The scheme uses a communication model involving a collection of viewers/subscribers, a content provider (IPTV), an advertiser, and a cloud server. To provide high quality directed advertising service, the advertiser can utilize not only demographic information of subscribers, but also their watching habits. The latter includes watching history, preferences for IPTV content and watching time, which are published on the cloud server periodically along with anonymized demographics (e.g. weekly). Since the published data may leak sensitive information about subscribers, it is safeguarded using cryptographic techniques in addition to the anonymization of demographics. The techniques used by the advertiser, which can be manifested in its queries to the cloud, are considered (trade) secrets and therefore are protected as well. The cloud is oblivious to the published data, the queries of the advertiser as well as its own responses to these queries. Only a legitimate advertiser, endorsed with a so-called trapdoor by the IPTV, can query the cloud and utilize the query results. The performance of the proposed scheme is evaluated with experiments, which show that the scheme is suitable for practical usage
CHORUS Deliverable 3.4: Vision Document
The goal of the CHORUS Vision Document is to create a high level vision on audio-visual search engines in order to give guidance to the future R&D work in this area and to highlight trends and challenges in this domain. The vision of CHORUS is strongly connected to the CHORUS Roadmap Document (D2.3). A concise document integrating the outcomes of the two deliverables will be prepared for the end of the project (NEM Summit)
UNDERSTANDING USER PERCEPTIONS AND PREFERENCES FOR MASS-MARKET INFORMATION SYSTEMS â LEVERAGING MARKET RESEARCH TECHNIQUES AND EXAMPLES IN PRIVACY-AWARE DESIGN
With cloud and mobile computing, a new category of software products emerges as mass-market information systems (IS) that addresses distributed and heterogeneous end-users. Understanding user requirements and the factors that drive user adoption are crucial for successful design of such systems. IS research has suggested several theories and models to explain user adoption and intentions to use, among them the IS Success Model and the Technology Acceptance Model (TAM). Although these approaches contribute to theoretical understanding of the adoption and use of IS in mass-markets, they are criticized for not being able to drive actionable insights on IS design as they consider the IT artifact as a black-box (i.e., they do not sufficiently address the system internal characteristics). We argue that IS needs to embrace market research techniques to understand and empirically assess user preferences and perceptions in order to integrate the "voice of the customer" in a mass-market scenario. More specifically, conjoint analysis (CA), from market research, can add user preference measurements for designing high-utility IS. CA has gained popularity in IS research, however little guidance is provided for its application in the domain. We aim at supporting the design of mass-market IS by establishing a reliable understanding of consumerâs preferences for multiple factors combing functional, non-functional and economic aspects. The results include a âFramework for Conjoint Analysis Studies in ISâ and methodological guidance for applying CA. We apply our findings to the privacy-aware design of mass-market IS and evaluate their implications on user adoption. We contribute to both academia and practice. For academia, we contribute to a more nuanced conceptualization of the IT artifact (i.e., system) through a feature-oriented lens and a preference-based approach. We provide methodological guidelines that support researchers in studying user perceptions and preferences for design variations and extending that to adoption. Moreover, the empirical studies for privacy- aware design contribute to a better understanding of the domain specific applications of CA for IS design and evaluation with a nuanced assessment of user preferences for privacy-preserving features. For practice, we propose guidelines for integrating the voice of the customer for successful IS design.
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Les technologies cloud et mobiles ont fait Ă©merger une nouvelle catĂ©gorie de produits informatiques qui sâadressent Ă des utilisateurs hĂ©tĂ©rogĂšnes par le biais de systĂšmes d'information (SI) distribuĂ©s. Les termes âSI de masseâ sont employĂ©s pour dĂ©signer ces nouveaux systĂšmes. Une conception rĂ©ussie de ceux-ci passe par une phase essentielle de comprĂ©hension des besoins et des facteurs d'adoption des utilisateurs. Pour ce faire, la recherche en SI suggĂšre plusieurs thĂ©ories et modĂšles tels que le âIS Success Modelâ et le âTechnology Acceptance Modelâ. Bien que ces approches contribuent Ă la comprĂ©hension thĂ©orique de l'adoption et de l'utilisation des SI de masse, elles sont critiquĂ©es pour ne pas ĂȘtre en mesure de fournir des informations exploitables sur la conception de SI car elles considĂšrent l'artefact informatique comme une boĂźte noire. En dâautres termes, ces approches ne traitent pas suffisamment des caractĂ©ristiques internes du systĂšme. Nous soutenons que la recherche en SI doit adopter des techniques d'Ă©tude de marchĂ© afin de mieux intĂ©grer les exigences du client (âVoice of Customerâ) dans un scĂ©nario de marchĂ© de masse. Plus prĂ©cisĂ©ment, l'analyse conjointe (AC), issue de la recherche sur les consommateurs, peut contribuer au dĂ©veloppement de systĂšme SI Ă forte valeur d'usage. Si lâAC a gagnĂ© en popularitĂ© au sein de la recherche en SI, des recommandations quant Ă son utilisation dans ce domaine restent rares. Nous entendons soutenir la conception de SI de masse en facilitant une identification fiable des prĂ©fĂ©rences des consommateurs sur de multiples facteurs combinant des aspects fonctionnels, non-fonctionnels et Ă©conomiques. Les rĂ©sultats comprennent un âCadre de rĂ©fĂ©rence pour les Ă©tudes d'analyse conjointe en SIâ et des recommandations mĂ©thodologiques pour l'application de lâAC. Nous avons utilisĂ© ces contributions pour concevoir un SI de masse particuliĂšrement sensible au respect de la vie privĂ©e des utilisateurs et nous avons Ă©valuĂ© lâimpact de nos recherches sur l'adoption de ce systĂšme par ses utilisateurs. Ainsi, notre travail contribue tant Ă la thĂ©orie quâĂ la pratique des SI. Pour le monde universitaire, nous contribuons en proposant une conceptualisation plus nuancĂ©e de l'artefact informatique (c'est-Ă -dire du systĂšme) Ă travers le prisme des fonctionnalitĂ©s et par une approche basĂ©e sur les prĂ©fĂ©rences utilisateurs. Par ailleurs, les chercheurs peuvent Ă©galement s'appuyer sur nos directives mĂ©thodologiques pour Ă©tudier les perceptions et les prĂ©fĂ©rences des utilisateurs pour diffĂ©rentes variations de conception et Ă©tendre cela Ă l'adoption. De plus, nos Ă©tudes empiriques sur la conception dâun SI de masse sensible au respect de la vie privĂ©e des utilisateurs contribuent Ă une meilleure comprĂ©hension de lâapplication des techniques CA dans ce domaine spĂ©cifique. Nos Ă©tudes incluent notamment une Ă©valuation nuancĂ©e des prĂ©fĂ©rences des utilisateurs sur des fonctionnalitĂ©s de protection de la vie privĂ©e. Pour les praticiens, nous proposons des lignes directrices qui permettent dâintĂ©grer les exigences des clients afin de concevoir un SI rĂ©ussi
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Open educational resources for all? Comparing user motivations and characteristics across The Open Universityâs iTunes U channel and OpenLearn platform.
With the rise in access to mobile multimedia devices, educational institutions have exploited the iTunes U platform as an additional channel to provide free educational resources with the aim of profile-raising and breaking down barriers to education. For those prepared to invest in content preparation, it is possible to produce interactive, portable material that can be made available globally. Commentators have questioned both the financial implications for platform-specific content production, and the availability of devices for learners to access it (Osborne, 2012).
The Open University (OU) makes its free educational resources available on iTunes U and via its web-based open educational resources (OER) platform, OpenLearn. The OUâs OER on iTunes U reached the 60 million download mark in 2013; its OpenLearn platform boasts 27 million unique visitors since 2006. This paper reports the results of a large-scale study of users of the OUâs iTunes U channel and OpenLearn platform. A survey of several thousand users revealed key differences in demographics between those accessing OER via the web and via iTunes U. In addition, the data allowed comparison between three groups: formal learners, informal learners and educators.
The study raises questions about whether university-provided OER meet the needs of users and makes recommendations for how content can be modified to suit their needs. As the publishing of OER becomes core to business, we reflect on reasons why understanding usersâ motivations and demographics is vital, allowing for needs-led resource provision and content that is adapted to best achieve learner satisfaction, and to deliver institutionsâ social mission
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Developing sustainable business models for institutionsâ provision of open educational resources: Learning from OpenLearn usersâ motivations and experiences
Universities across the globe have, for some time, been exploring the possibilities for achieving public benefit and generating business and visibility through releasing and sharing open educational resources (OER). Many have written about the need to develop sustainable and profitable business models around the production and release of OER. Downes (2006), for example, has questioned the financial sustainability of OER production at scale. Many of the proposed business models focus on OERâs value in generating revenue and detractors of OER have questioned whether they are in competition with formal education.
This paper reports on a study intended to broaden the conversation about OER business models to consider the motivations and experiences of OER users as the basis for making a better informed decision about whether OER and formal learning are competitive or complementary with each other. The study focused on OpenLearn - the Open Universityâs (OU) web-based platform for OER, which hosts hundreds of online courses and videos and is accessed by over 3,000,000 users a year. A large scale survey and follow-up interviews with OpenLearn users worldwide revealed that university provided OER can offer learners a bridge to formal education, allowing them to try out a subject before registering on a formal course and to build confidence in their abilities as learners. In addition, it was found that using OER during formal paid-for study can improve learnersâ performance and self-reliance, leading to increased retention and satisfaction with the learning experience
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