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When pieces from an individual's personal information available online are connected over time and across multiple platforms, this more complete digital trace can give unintended insights into their life and opinions. In a data narrative interview study with 26 currently employed participants, we examined risks and harms to individuals and employers when others joined the dots between their online information. We discuss the themes of visibility and self-disclosure, unintentional information leakage and digital privacy literacies constructed from our analysis. We contribute insights not only into people's difficulties in recalling and conceptualising their digital traces but of subsequently envisioning how their online information may be combined, or (re)identified across their traces and address a current gap in research by showing that awareness is lacking around the potential for personal information to be correlated by and made coherent to/by others, posing risks to individuals, employers, and even the state. We touch on inequalities of privacy, freedom and legitimacy that exist for different groups with regard to what they make (or feel compelled to make) available online and we contribute to current methodological work on the use of sketching to support visual sense making in data narrative interviews. We conclude by discussing the need for interventions that support personal reflection on the potential visibility of combined digital traces to spotlight hidden vulnerabilities, and promote more proactive action about what is shared and not shared online., Comment: Accepted to CSCW 2022, Taipei

We present a novel blockchain based service for proving the provenance of online digital identity, exposed as an assistive tool to help non-expert users make better decisions about whom to trust online. Our service harnesses the digital personhood (DP); the longitudinal and multi-modal signals created through users' lifelong digital interactions, as a basis for evidencing the provenance of identity. We describe how users may exchange trust evidence derived from their DP, in a granular and privacy-preserving manner, with other users in order to demonstrate coherence and longevity in their behaviour online. This is enabled through a novel secure infrastructure combining hybrid on- and off-chain storage combined with deep learning for DP analytics and visualization. We show how our tools enable users to make more effective decisions on whether to trust unknown third parties online, and also to spot behavioural deviations in their own social media footprints indicative of account hijacking., Comment: Submitted to IEEE TSC Special Issue on Blockchain Services, May 2019