Collective awareness platforms and digital social innovation mediating consensus seeking in problem situations

In this paper we show the results of our studies carried out in the framework of the European Project SciCafe2.0 in the area of Participatory Engagement models. We present a methodological approach built on participative engagements models and holistic framework for problem situation clarification and solution impacts assessment. Several online platforms for social engagement have been analysed to extract the main patterns of participative engagement. We present our own experiments through the SciCafe2.0 Platform and our insights from requirements elicitation

When PETs misbehave: A Contextual Integrity analysis

Privacy enhancing technologies, or PETs, have been hailed as a promising means to protect privacy without compromising on the functionality of digital services. At the same time, and partly because they may encode a narrow conceptualization of privacy as confidentiality that is popular among policymakers, engineers and the public, PETs risk being co-opted to promote privacy-invasive practices. In this paper, we resort to the theory of Contextual Integrity to explain how privacy technologies may be misused to erode privacy. To illustrate, we consider three PETs and scenarios: anonymous credentials for age verification, client-side scanning for illegal content detection, and homomorphic encryption for machine learning model training. Using the theory of Contextual Integrity, we reason about the notion of privacy that these PETs encode, and show that CI enables us to identify and reason about the limitations of PETs and their misuse, and which may ultimately lead to privacy violations

Systematization of threats and requirements for private messaging with untrusted servers. The case of E-mailing and instant messaging

Modern email and instant messaging applications often offer private communications. In doing so, they share common concerns about how security and privacy can be compromised, how they should face similar threats, and how to comply with comparable system requirements. Assuming a scenario where servers may not be trusted, we review and analyze a list of threats specifically against message delivering, archiving, and contact synchronization. We also describe a list of requirements intended for whom undertakes the task of implementing secure and private messaging. The cryptographic solutions available to mitigate the threats and to comply with the requirements may differ, as the two applications are built on different assumptions and technologies

Unsupervised Neural Stylistic Text Generation using Transfer learning and Adapters

Research has shown that personality is a key driver to improve engagement and user experience in conversational systems. Conversational agents should also maintain a consistent persona to have an engaging conversation with a user. However, text generation datasets are often crowd sourced and thereby have an averaging effect where the style of the generation model is an average style of all the crowd workers that have contributed to the dataset. While one can collect persona-specific datasets for each task, it would be an expensive and time consuming annotation effort. In this work, we propose a novel transfer learning framework which updates only $0.3\%$ of model parameters to learn style specific attributes for response generation. For the purpose of this study, we tackle the problem of stylistic story ending generation using the ROC stories Corpus. We learn style specific attributes from the PERSONALITY-CAPTIONS dataset. Through extensive experiments and evaluation metrics we show that our novel training procedure can improve the style generation by 200 over Encoder-Decoder baselines while maintaining on-par content relevance metrics wit