Demo Data from EDBT 2026 / reviewers in the wild / expert
Brooke Bullek
dblp:199/3072
· DBLP profile ↗
1ranked-venue papers
1as first author
0since 2021 · last 2017
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Human-computer interaction and ubiquitous computing · 1 · 1 first-author
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 |
Privacy and data protection · 87% Usable security · 13% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Privacy and data protection
differential privacy |
0.3 | 1 | 2017 | Towards Understanding Differential Privacy: When Do People Trust Randomized Response Technique? · CHI 2017 |
Privacy and data protection › differential privacy › local differential privacy
randomized response |
0.3 | 1 | 2017 | Towards Understanding Differential Privacy: When Do People Trust Randomized Response Technique? · CHI 2017 |
Usable security › usable privacy
privacy decision-making |
0.1 | 1 | 2017 | Towards Understanding Differential Privacy: When Do People Trust Randomized Response Technique? · CHI 2017 |
Methods — techniques the papers use, named apart from their topics
user study · 0.3survey · 0.3
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2017 | Towards Understanding Differential Privacy: When Do People Trust Randomized Response Technique?abstractAs a consequence of living in a data ecosystem, we often relinquish personal information to be used in contexts in which we have no control. In this paper, we begin to examine the usability of differential privacy, a mechanism that proposes to promise privacy with a mathematical "proof" to the data donor. Do people trust this promise and adjust their privacy decisions if the interfaces through which they interact make differential privacy less opaque? In a study with 228 participants, we measured comfort, understanding, and trust using a variant of differential privacy known as Randomized Response Technique (RRT). We found that allowing individuals to see the amount of obfuscation applied to their responses increased their trust in the privacy-protecting mechanism. However, participants who associated obfuscating privacy mechanisms with deception did not make the "safest" privacy decisions, even as they demonstrated an understanding of RRT. We demonstrate that prudent privacy-related decisions can be cultivated with simple explanations of usable privacy. Brooke Bullek, Stephanie Garboski, Darakhshan J. Mir, Evan M. Peck |
CHI | 1 |