Demo Data from VLDB 2026 / reviewers in the wild / expert
Shiva Mayahi
dblp:334/4115
· DBLP profile ↗
1ranked-venue papers
1as first author
1since 2021 · last 2026
0000-0003-3690-7424ORCID · reported
Domains — the database's venue-derived domains; a paper can count in several
Human-computer interaction and ubiquitous computing · 1 · 1 first-author · 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.
| Human-computer interaction and pervasive computing
1 paper |
Usability and user experience research · 77% Human-AI interaction · 23% | |
| Network and information security
1 paper |
Privacy and data protection · 100% |
Topics — the 1 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Privacy and data protection
privacy compliance |
1.0 | 1 | 2026 | Tinker, Tailor, Trust: How Developers Create Privacy Policies With and Without AI · CHI 2026 |
Methods — techniques the papers use, named apart from their topics
observation study · 2.0interview study · 2.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2026 | Tinker, Tailor, Trust: How Developers Create Privacy Policies With and Without AIabstractFor mobile developers to comply with privacy regulations, they must create privacy policies that accurately describe their apps’ data practices. This requires a complete understanding of their apps’ behaviors, including those of embedded third-party SDKs. Despite the complexity of this process, little is known about how privacy policies are created and validated. To investigate, we interviewed 20 developers from around the world about their processes, also observing them use a large language model (LLM) to prepare privacy policies for their apps. We found that developers struggle with collecting information about third-party SDKs, even when they use LLMs, and feel uncertain about the legal validity of LLM outputs. Many developers do not seek legal assistance and believe that, as long as app stores accept their privacy policies, they are protected. Our findings suggest that reliance on LLMs and developers’ desire to externalize validation may result in increasingly unreliable privacy policies. Shiva Mayahi, Noura Alomar, Nathan Malkin |
CHI | 1 |