Demo Data from VLDB 2026 / reviewers in the wild / expert
Michael Andrejczuk
dblp:318/9576
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2022
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Databases, data management, data science and information retrieval · 1 · 1 since 2021Applied, interdisciplinary, general and emerging computing · 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 |
Privacy and data protection · 100% | |
| Artificial intelligence
1 paper |
Generative modeling · 100% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Machine learning › Generative modeling
variational autoencoder |
0.6 | 1 | 2022 | DP-VAE: Human-Readable Text Anonymization for Online Reviews with Differentially Private Variational Autoencoders · WWW 2022 |
Privacy and data protection › differential privacy › synthetic data generation
differentially private text generation |
0.6 | 1 | 2022 | DP-VAE: Human-Readable Text Anonymization for Online Reviews with Differentially Private Variational Autoencoders · WWW 2022 |
Privacy and data protection › anonymization
text anonymization |
0.6 | 1 | 2022 | DP-VAE: Human-Readable Text Anonymization for Online Reviews with Differentially Private Variational Autoencoders · WWW 2022 |
Methods — techniques the papers use, named apart from their topics
variational autoencoder · 1.1differential privacy · 1.1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2022 | DP-VAE: Human-Readable Text Anonymization for Online Reviews with Differentially Private Variational AutoencodersabstractWhile vast amounts of personal data are shared daily on public online platforms and used by companies and analysts to gain valuable insights, privacy concerns are also on the rise: Modern authorship attribution techniques have proven effective at identifying individuals from their data, such as their writing style or behavior of picking and judging movies. It is hence crucial to develop data sanitization methods that allow sharing of users’ data while protecting their privacy and preserving quality and content of the original data. Benjamin Weggenmann, Valentin Rublack, Michael Andrejczuk, Justus Mattern, Florian Kerschbaum |
WWW | 3 |