EDBT 2026 Demo / reviewers in the wild / expert
Sophie Veys
dblp:264/7270
· DBLP profile ↗
2ranked-venue papers
0as first author
0since 2021 · last 2020
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Security and privacy · 1Human-computer interaction and ubiquitous computing · 1
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
2 papers |
Privacy and data protection · 88% Usable security · 12% | |
| Human-computer interaction and pervasive computing
1 paper |
Collaborative and social computing · 100% |
Topics — the 5 heaviest of 5, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Collaborative and social computing › crowdsourcing
crowd work |
0.4 | 1 | 2020 | Taking Data Out of Context to Hyper-Personalize Ads: Crowdworkers' Privacy Perceptions and Decisions to Disclose Private Information · CHI 2020 |
Privacy and data protection › privacy analysis › privacy models
contextual integrity |
0.4 | 1 | 2020 | Taking Data Out of Context to Hyper-Personalize Ads: Crowdworkers' Privacy Perceptions and Decisions to Disclose Private Information · CHI 2020 |
Privacy and data protection › web privacy
online advertising privacy |
0.4 | 1 | 2020 | What Twitter Knows: Characterizing Ad Targeting Practices, User Perceptions, and Ad Explanations Through Users' Own Twitter Data · USENIX Security Symposium 2020 |
Usable security
privacy control |
0.1 | 1 | 2020 | Taking Data Out of Context to Hyper-Personalize Ads: Crowdworkers' Privacy Perceptions and Decisions to Disclose Private Information · CHI 2020 |
Privacy and data protection
social network privacy |
0.1 | 1 | 2020 | What Twitter Knows: Characterizing Ad Targeting Practices, User Perceptions, and Ad Explanations Through Users' Own Twitter Data · USENIX Security Symposium 2020 |
Methods — techniques the papers use, named apart from their topics
survey · 0.9human-subjects experiment · 0.9user study · 0.4data analysis · 0.4
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2020 | Taking Data Out of Context to Hyper-Personalize Ads: Crowdworkers' Privacy Perceptions and Decisions to Disclose Private InformationabstractData brokers and advertisers increasingly collect data in one context and use it in another. When users encounter a misuse of their data, do they subsequently disclose less information? We report on human-subjects experiments with 25 in-person and 280 online participants. First, participants provided personal information amidst distractor questions. A week later, while participants completed another survey, they received either a robotext or online banner ad seemingly unrelated to the study. Half of the participants received an ad containing their name, partner's name, preferred cuisine, and location; others received a generic ad. We measured how many of 43 potentially invasive questions participants subsequently chose to answer. Participants reacted negatively to the personalized ad, yet answered nearly all invasive questions accurately. We unpack our results relative to the privacy paradox, contextual integrity, and power dynamics in crowdworker platforms. Julia Hanson, Miranda Wei, Sophie Veys, Matthew Kugler, Lior Jacob Strahilevitz, Blase Ur |
CHI | 3 |
| 2020 | What Twitter Knows: Characterizing Ad Targeting Practices, User Perceptions, and Ad Explanations Through Users' Own Twitter Data
Miranda Wei, Madison Stamos, Sophie Veys, Nathan Reitinger, Justin Goodman, Margot Herman, Dorota Filipczuk, Ben Weinshel, Michelle L. Mazurek, Blase Ur |
USENIX Security Symposium | 3 |