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Michael Andrejczuk

dblp:318/9576 · DBLP profile ↗
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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

TopicWeightPapersLastEvidence papers
Machine learning › Generative modeling
variational autoencoder
0.612022
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
0.612022
DP-VAE: Human-Readable Text Anonymization for Online Reviews with Differentially Private Variational Autoencoders · WWW 2022
Privacy and data protection › anonymization
text anonymization
0.612022
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
YearPublicationVenuePosition
2022 DP-VAE: Human-Readable Text Anonymization for Online Reviews with Differentially Private Variational Autoencoders
abstract
While 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
WWW3