The Curious Case of the PDF Converter that Likes Mozart: Dissecting and Mitigating the Privacy Risk of Personal Cloud Apps

Third party apps that work on top of personal cloud services such as Google Drive and Dropbox, require access to the user's data in order to provide some functionality. Through detailed analysis of a hundred popular Google Drive apps from Google's Chrome store, we discover that the existing permission model is quite often misused: around two thirds of analyzed apps are over-privileged, i.e., they access more data than is needed for them to function. In this work, we analyze three different permission models that aim to discourage users from installing over-privileged apps. In experiments with 210 real users, we discover that the most successful permission model is our novel ensemble method that we call Far-reaching Insights. Far-reaching Insights inform the users about the data-driven insights that apps can make about them (e.g., their topics of interest, collaboration and activity patterns etc.) Thus, they seek to bridge the gap between what third parties can actually know about users and users perception of their privacy leakage. The efficacy of Far-reaching Insights in bridging this gap is demonstrated by our results, as Far-reaching Insights prove to be, on average, twice as effective as the current model in discouraging users from installing over-privileged apps. In an effort for promoting general privacy awareness, we deploy a publicly available privacy oriented app store that uses Far-reaching Insights. Based on the knowledge extracted from data of the store's users (over 115 gigabytes of Google Drive data from 1440 users with 662 installed apps), we also delineate the ecosystem for third-party cloud apps from the standpoint of developers and cloud providers. Finally, we present several general recommendations that can guide other future works in the area of privacy for the cloud

CHORUS Deliverable 2.2: Second report - identification of multi-disciplinary key issues for gap analysis toward EU multimedia search engines roadmap

After addressing the state-of-the-art during the first year of Chorus and establishing the existing landscape in multimedia search engines, we have identified and analyzed gaps within European research effort during our second year. In this period we focused on three directions, notably technological issues, user-centred issues and use-cases and socio- economic and legal aspects. These were assessed by two central studies: firstly, a concerted vision of functional breakdown of generic multimedia search engine, and secondly, a representative use-cases descriptions with the related discussion on requirement for technological challenges. Both studies have been carried out in cooperation and consultation with the community at large through EC concertation meetings (multimedia search engines cluster), several meetings with our Think-Tank, presentations in international conferences, and surveys addressed to EU projects coordinators as well as National initiatives coordinators. Based on the obtained feedback we identified two types of gaps, namely core technological gaps that involve research challenges, and “enablers”, which are not necessarily technical research challenges, but have impact on innovation progress. New socio-economic trends are presented as well as emerging legal challenges

Agora: A Knowledge Marketplace for Machine Learning

More and more data are becoming part of people\u27s lives. With the popularization of technologies like sensors, and the Internet of Things, data gathering is becoming possible and accessible for users. With these data in hand, users should be able to extract insights from them, and they want results as soon as possible. Average users have little or no experience in data analytics and machine learning and are not great observers who can collect enough data to build their own machine learning models. With large quantities of similar data being generated around the world and many machine learning models being used, it should be possible to use additional data and existing models to create accurate machine learning models for these users. This thesis proposes Agora, a Web-based marketplace where users can share their data and machine learning models with other users with small datasets and little experience. This thesis includes an overview of all the components that make up Agora, as well as details of two of its main components: Hephaestus and Sibyl. Hephaestus is a domain adaptation method for multi-feature regression models with seasonal adjustment, which can improve predictions for small datasets using information from additional datasets. Hephaestus works in the pre- and post- processing phases, making it possible to work with any standard machine learning algorithm. As a case study, we built predictive models using the proposed method to predict school energy consumption with only one month of data, improving accuracy to the same level as if 12 months of data were being used. Sibyl is a flexible, scalable and non-blocking machine learning as a service, which facilitates the creation of multiple predictive models and running them at the same time. As a case study, we implemented Sibyl equipped with three machine learning algorithms to show the flexibility of adding new algorithms. We also executed three models at the same time to demonstrate that they can run without interference from another model. The results obtained in this research demonstrates the concept of Agora. Users can share the same platform to provide or consume knowledge and create multiple concurrent machine learning models

Visual Search at eBay

In this paper, we propose a novel end-to-end approach for scalable visual search infrastructure. We discuss the challenges we faced for a massive volatile inventory like at eBay and present our solution to overcome those. We harness the availability of large image collection of eBay listings and state-of-the-art deep learning techniques to perform visual search at scale. Supervised approach for optimized search limited to top predicted categories and also for compact binary signature are key to scale up without compromising accuracy and precision. Both use a common deep neural network requiring only a single forward inference. The system architecture is presented with in-depth discussions of its basic components and optimizations for a trade-off between search relevance and latency. This solution is currently deployed in a distributed cloud infrastructure and fuels visual search in eBay ShopBot and Close5. We show benchmark on ImageNet dataset on which our approach is faster and more accurate than several unsupervised baselines. We share our learnings with the hope that visual search becomes a first class citizen for all large scale search engines rather than an afterthought.Comment: To appear in 23rd SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2017. A demonstration video can be found at https://youtu.be/iYtjs32vh4

Socialising around media. Improving the second screen experience through semantic analysis, context awareness and dynamic communities

SAM is a social media platform that enhances the experience of watching video content in a conventional living room setting, with a service that lets the viewer use a second screen (such as a smart phone) to interact with content, context and communities related to the main video content. This article describes three key functionalities used in the SAM platform in order to create an advanced interactive and social second screen experience for users: semantic analysis, context awareness and dynamic communities. Both dataset-based and end user evaluations of system functionalities are reported in order to determine the effectiveness and efficiency of the components directly involved and the platform as a whole

An assessment of brand experience knowledge literature: using bibliometric data to identify future research direction

There is wide consensus that the brand experience literature (BEL) suffers from a deficit in conceptual works. This study argues that, for brand experience research to overcome its conceptual insipidity, it must reexamine the core of its intellectual structure to rediscover what ‘an experience provided by brands’ truly implies. The purpose of this paper is to reconceptualize and present a future research framework for research into the concept of brand experience, by identifying both the core and peripheral sources of knowledge of the concept and its association with brand meaning. Through a bibliometric process covering 136 articles published between 2002 and 2018, resulting in a database of 2,698 citations, this brand experience conceptual paper fills a critical research gap by providing the first full-scale bibliometric study to date of the BEL, using a combination of high citation and co-citation metrics. Based on this conceptual reorientation, a matrix for future development is presented, enabling the reader to visualize the scope and breadth of potential brand experience research horizons in areas relating to customer experience, consumer-brand relationship, online brand experience and sensory brand experience. The four approaches listed in the matrix – firm-based, social constructionist, virtuality and embodiment – provide a roadmap for future brand experience research undertakings to explore the rich potential of experience evoked by brands