REWARD : ontology for reward schemes

Rewarding people is common in several contexts, such as human resource management and crowdsourcing applications. However, designing a reward strategy is not straightforward, as it requires considering different parameters. These parameters include, for example, management of rewarding tasks and identifying critical features, such as the type of rewards and possibilities such as gamification. Moreover, the lack of a common terminology introduces the problem of communication among experts and prevents integration among different reward strategies. An ontology can offer a common understanding among domain experts and flexible management of rewarding parameters. Apart from that, an ontology can also help in the interrelationship and integration between different reward schemes employed by different service providers. In this paper, we present REWARD, a general-purpose ontology for capturing various common features of diverse reward schemes. This ontology is a result of the CAP-A European project and its application to the crowdsourcing domain, but it is designed to cover different needs and domains

CAP-A : a suite of tools for data privacy evaluation of mobile applications

The utilisation of personal data by mobile apps is often hidden behind vague Privacy Policy documents, which are typically lengthy, difficult to read (containing legal terms and definitions) and frequently changing. This paper discusses a suite of tools developed in the context of the CAP-A project, aiming to harness the collective power of users to improve their privacy awareness and to promote privacy-friendly behaviour by mobile apps. Through crowdsourcing techniques, users can evaluate the privacy friendliness of apps, annotate and understand Privacy Policy documents, and help other users become aware of privacy-related aspects of mobile apps and their implications, whereas developers and policy makers can identify trends and the general stance of the public in privacy-related matters. The tools are available for public use in: https://cap-a.eu/tools/

Επεξεργασία κ-κορυφαίων ερωτήσεων σε ομότιμα δίκτυα

The idea of Peer-to-peer (P2P) computing offers new opportunities for building highly distributed data systems. As the idea of Semantic Web arisen a new category of peer-to-peer systems called Schema-Based come up. In Schema-Based P2P systems each peer is a whole database management system in itself. Each peer can use its own database schema, manages its own data and by this way keeps sovereignty over itself. Considering a Schema-Based peer-to-peer network our main goal is the easy sharing of knowledge bases which implies efficient exchange of data across the p2p network without consuming enough bandwidth. For this reason at first we suggest a suitable peer-to-peer architecture and a well defined query routing context. Our main contribution is the suggestion of a query routing strategy and a query processing strategy. The proposed query routing strategy directs the query only to a set of relevant peers in such way to avoid network traffic and bandwidth consumption. Our processing technique based on the idea of top-k queries that arisen from the research area of databases. Simply top-k queries return only the k best results according to a given criterion. Recently top-k retrieval algorithms for distributed networks have been presented at some approaches. After presenting these approaches and determining their advantages and drawbacks, we finally conclude the Hybrid Threshold (HT) algorithm could be the best solution for top-k processing in peer-to-peer networks. We extend HT and adapt it under our well defined peer-to-peer environment and in consequence we suggest two improved versions: HT-p2p and HT-p2p+. The first assumes that results are returned by executing an instance of the algorithm to a specified Super-Peer named at this case collector Super-Peer. The last assumes that results come from the combination of all top-k object set that are returned from each running instance of the algorithm to each specified contributor Super-Peer. In addition, since HT-p2p belong to score-based top-k algorithms so we study the problem of scoring objects and suggest accordingly three use cases of the algorithm. For the evaluation of HT-p2p and HT-p2p+ we implement a prototype system built upon JXTA platform. The results of the experiments upon HT-p2p system showed that our suggested algorithm is a scalable, and efficient enough top-k processing algorithm that could be used by any Super-Peer based peer-to-peer network.Τα ομότιμα δίκτυα (peer-to-peer (P2P) networks) παρέχουν πολλές δυνατότητες για την ανάπτυξη πλήρως κατανεμημένων συστημάτων διαχείρισης δεδομένων. Καθώς η ιδέα του σημασιολογικού ιστού άρχισε να εδραιώνεται, έκαναν την εμφάνιση τους τα ομότιμα συστήματα στα οποία κάθε κόμβος διαχειρίζεται μια ξεχωριστή βάση δεδομένων και για την οποία διατηρεί ένα συγκεκριμένο σχήμα. (Schema-Based peer-to-peer networks0.). Θεωρώντας ένα Schema-Based peer-to-peer network βασικός στόχος μας είναι ο εύκολος διαμοιρασμός της πληροφορίας με το ελάχιστο εύρος των δεδομένων που πρέπει να μετακινηθούν κατά μήκος του ομότιμου δικτύου. Για τον λόγο αυτό προτείνουμε μια κατάλληλη αρχιτεκτονική για το συνιστώμενο ομότιμο δίκτυο και ένα καλά ορισμένο πλαίσιο δρομολόγησης των ερωτήσεων. Η κεντρική συνεισφορά της εργασίας έγκειται στην πρόταση μιας ολοκληρωμένης στρατηγικής δρομολόγησης και επεξεργασίας της κάθε ερώτησης. Η προτεινόμενη στρατηγική δρομολόγησης αναλαμβάνει την κατεύθυνση της ερώτησης στους κατάλληλους κόμβους χωρίς να δημιουργεί αρκετή κυκλοφορία στο ομότιμο δίκτυο γεμίζοντας το με άσκοπα μηνύματα. Η προτεινόμενη στρατηγική επεξεργασίας βασίζεται στην ιδέα των κ-κορυφαίων ερωτήσεων η οποία πρωτοεμφανίστηκε στον τομέα των βάσεων δεδομένων. Οι κ-κορυφαίες ερωτήσεις επιστρέφουν τα κ καλύτερα αποτελέσματα δεδομένου κάποιου ορισμένου κριτηρίου. Πρόσφατα αυτή η ιδέα άρχισε να εφαρμόζεται σε κατανεμημένα δίκτυα. Αφού παρουσιάσουμε και αναλύσουμε τις υπάρχουσες προσεγγίσεις συμπεραίνουμε ότι ο υβριδικός αλγόριθμος (HT) ταιριάζει καλύτερα στο δικό μας σενάριο, γι αυτό τον επεκτείνουμε και τον προσαρμόζουμε στις απαιτήσεις του συγκεκριμένου κατανεμημένου περιβάλλοντος. Τελικά παρουσιάζουμε δύο εκδόσεις του βελτιωμένου μας αλγορίθμου (HT-p2p, HT-p2p+) ανάλογα με την περίπτωση χρήσης του. Επιπλέον, δεδομένου ότι ο αλγόριθμος ανήκει στην οικογένεια των βασιζόμενων σε σκορ αλγορίθμων, μελετάμε το πρόβλημα της βαθμολόγησης των αντικειμένων και προτείνουμε τρία σενάρια χρήσης για κάθε περίπτωση. Για την αποτίμηση του HT-p2p αλγορίθμου αναπτύξαμε ένα σύστημα χρησιμοποιώντας την τεχνολογία που παρέχει η πλατφόρμα JXTA Τα αποτελέσματα των πειραμάτων έδειξαν ότι η προτεινόμενος αλγόριθμος έχει καλή κλιμακωσιμότητα και είναι αποδοτικός σε κάθε ομότιμο δίκτυο που ακολουθεί την προτεινόμενη αρχιτεκτονική μας

CAP-A – Raising Privacy Awareness Depends on Us!

CAP-A is offering socio-technical tools to promote collective awareness and informed consent, whereby data collection and use by digital products are driven by the expectations and needs of the consumers themselves

Towards a collective awareness platform for privacy concerns and expectations

In an increasingly instrumented and inter-connected digital world, citizens generate vast amounts of data, much of it being valuable and a significant part of it being personal. However, controlling who can collect it, limiting what they can do with it, and determining how best to protect it, remain deeply undecided issues. This paper proposes CAPrice, a socio-technical solution based on collective awareness and informed consent, whereby data collection and use by digital products are driven by the expectations and needs of the consumers themselves, through a collaborative participatory process and the configuration of collective privacy norms. The proposed solution relies on a new innovation model that complements existing top-down approaches to data protection, which mainly rely on technical or legal provisions. Ultimately, the CAPrice ecosystem will strengthen the trust bond between service developers and users, encouraging innovation and empowering the individuals to promote their privacy expectations as a quantifiable, community-generated request.acceptedVersio

Towards a collective awareness platform for privacy concerns and expectations

In an increasingly instrumented and inter-connected digital world, citizens generate vast amounts of data, much of it being valuable and a significant part of it being personal. However, controlling who can collect it, limiting what they can do with it, and determining how best to protect it, remain deeply undecided issues. This paper proposes CAPrice, a socio-technical solution based on collective awareness and informed consent, whereby data collection and use by digital products are driven by the expectations and needs of the consumers themselves, through a collaborative participatory process and the configuration of collective privacy norms. The proposed solution relies on a new innovation model that complements existing top-down approaches to data protection, which mainly rely on technical or legal provisions. Ultimately, the CAPrice ecosystem will strengthen the trust bond between service developers and users, encouraging innovation and empowering the individuals to promote their privacy expectations as a quantifiable, community-generated request

A Rewarding Framework for Crowdsourcing to Increase Privacy Awareness

Part 5: Potpourri IInternational audienceDigital applications typically describe their privacy policy in lengthy and vague documents (called PrPs), but these are rarely read by users, who remain unaware of privacy risks associated with the use of these digital applications. Thus, users need to become more aware of digital applications’ policies and, thus, more confident about their choices. To raise privacy awareness, we implemented the CAP-A portal, a crowdsourcing platform which aggregates knowledge as extracted from PrP documents and motivates users in performing privacy-related tasks. The Rewarding Framework is one of the most critical components of the platform. It enhances user motivation and engagement by combining features from existing successful rewarding theories. In this work, we describe this Rewarding Framework, and show how it supports users to increase their privacy knowledge level by engaging them to perform privacy-related tasks, such as annotating PrP documents in a crowdsourcing environment. The proposed Rewarding Framework was validated by pilots ran in the frame of the European project CAP-A and by a user evaluation focused on its impact in terms of engagement and raising privacy awareness. The results show that the Rewarding Framework improves engagement and motivation, and increases users’ privacy awareness