Consumers are largely unaware regarding the use being made to the data that they generate through smart devices, or their GDPR-compliance, since such information is typically hidden behind vague privacy policy documents, which are often lengthy, difficult to read (containing legal terms and definitions) and frequently changing. This paper describes the activities of the CAP-A project, whose aim is to apply crowdsourcing techniques to evaluate the privacy friendliness of apps, and to allow users to better understand the content of Privacy Policy documents and, consequently, the privacy implications of using any given mobile app. To achieve this, we developed a set of tools that aim at assisting users to express their own privacy concerns and expectations and assess the mobile apps’ privacy properties through collective intelligence.

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/.

Digital 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.

Today's complex world requires state-of-the-art data analysis over truly massive data sets. These data sets can be stored persistently in databases or flat files, or can be generated in realtime in a continuous manner. An associated set is a collection of data sets, annotated by the values of a domain D. These data sets are populated using a data source according to a condition θ and the annotated value. An ASsociated SET (ASSET) query consists of repeated, successive, interrelated definitions of associated sets, put together in a column-wise fashion, resembling a spreadsheet document. We present DataMingler, a powerful GUI to express and manage ASSET queries, data sources and aggregate functions and the ASSET Query Engine (QE) to efficiently evaluate ASSET queries. We argue that ASSET queries: a) constitute a useful class of OLAP queries, b) are suitable for distributed processing settings, and c) extend the MapReduce paradigm in a declarative way.