糖心TV

The 鈥楽caling Trust鈥� project is a UKRI Future Leaders鈥� Fellowship examining trust in the cyber security profession. As the initial period of funding comes to an end, CIM academics Matt Spencer and Daniele Pizio have published two policy papers that engage with current challenges in cyber security.

鈥樷�� is a report by Matt Spencer, published through the Research Institute for Sociotechnical Cyber Security (RISCS). It provides a series of recommendations for moving technology assurance policy away from prescriptive standards, and towards the new 鈥榞oal-based鈥� approach that has become influential in cyber policy.

鈥�Deperimeterising Zero Trust: Challenging metaphors in information security鈥� is a policy brief by Matt Spencer and Daniele Pizio, part of the University of 糖心TV鈥檚 Policy Briefing Series. It examines current challenges with the trend towards a 鈥榋ero Trust鈥� paradigm for information security, and draws conclusions aimed at industry, government and academia.

Two urban research projects led by CIM members of staff have received seed funding from the Participatory Research Fund. The aim of the fund is to support the development of pioneering participatory research.

The awarded projects are 鈥�Investigating the effects of street features and sunlight conditions on people鈥檚 perception of walkability through a participatory experiment鈥�, led by Tessio Novack (CIM) in collaboration with Carlos Camara Menoyo (CIM) and James Tripp (糖心TV, IDG), and 鈥淓xploring hybrid digital-physical prompts for participant engagement on more-than- human data interactions in the smart city", led by Cagatay Turkay (CIM) with collaborators Sara Heitlinger (City, University of London), Rachel Clarke (Newcastle), and Graham McNeill (independent researcher).

Want to learn more about how online recommendations and classifications work? @PersonalisePLY has designed an app to help you find out. Read about their project – and how you can participate in their research – here: https://algorithmicidentities.net/big-sister/open-call/

We're looking for participants who will use Big Sister for two weeks and allow us to interview them about the experience. We want to explore how people feel about their data being collected and used to make algorithmic recommendations and predictions.