Demo Data from VLDB 2026 / reviewers in the wild / expert
Helen S. Jones
dblp:174/2867
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2022
0000-0002-2716-051XORCID · reported
Domains — the database's venue-derived domains; a paper can count in several
Software engineering, systems software and programming languages · 1 · 1 since 2021
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Network and information security
1 paper |
Authentication and access control · 44% Privacy and data protection · 44% Blockchain and cryptocurrency security · 13% |
Topics — the 1 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Authentication and access control › identity management
digital identity |
0.6 | 1 | 2022 | TAPESTRY: A De-Centralized Service for Trusted Interaction Online · IEEE Trans. Serv. Comput. 2022 |
Methods — techniques the papers use, named apart from their topics
hybrid on-chain and off-chain storage · 0.6deep learning · 0.6
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2022 | TAPESTRY: A De-Centralized Service for Trusted Interaction OnlineabstractWe present a novel de-centralised 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. Daniel Cooper, John P. Collomosse, Constantin Catalin Dragan, Mark Manulis, Jamie Steane, Arthi Kanchana Manohar, Jo Briggs, Helen S. Jones, Wendy Moncur |
IEEE Trans. Serv. Comput. | 9 |