- Path:
-
ColorChart
Periodical
- Title:
- Information technology
- Publication:
-
Berlin: De Gruyter
- Note:
- Gesehen am 16.01.18
- C!URL-Ä(13-08-13)
- Text meist engl., früher dt.
- Scope:
- Online-Ressource
- ISSN:
- 2196-7032
- ZDB-ID:
-
2028598-X
- VÖBB-Katalog:
- 35425745
- Keywords:
- Technische Informatik ; Informationstechnik ; Zeitschrift ; Zeitschrift ; Online-Publikation ; Zeitschrift ; Online-Publikation
- Classification:
- Informatik
- Technik
- Collection:
- Informatik
- Technik
- Copyright:
- Rights reserved
- Accessibility:
- Eingeschränkter Zugang mit Nutzungsbeschränkungen
- Title:
- Information technology
- Publication:
-
Berlin: De Gruyter
- Note:
- Gesehen am 16.01.18
- C!URL-Ä(13-08-13)
- Text meist engl., früher dt.
- Scope:
- Online-Ressource
- ISSN:
- 2196-7032
- ZDB-ID:
-
2028598-X
- VÖBB-Katalog:
- 35425745
- Keywords:
- Technische Informatik ; Informationstechnik ; Zeitschrift ; Zeitschrift ; Online-Publikation ; Zeitschrift ; Online-Publikation
- Classification:
- Informatik
- Technik
- Collection:
- Informatik
- Technik
- Copyright:
- Rights reserved
- Accessibility:
- Eingeschränkter Zugang mit Nutzungsbeschränkungen
Article
- Title:
- A self-portrayal of GI Junior Fellow Franziska Boenisch: trustworthy machine learning for individuals
- Publication:
-
Berlin: De Gruyter, 2025
- Language:
- English
- Information:
- Abstract: Machine learning (ML) is increasingly deployed in critical domains such as healthcare, finance, and autonomous driving, where the use of sensitive data raises significant privacy challenges. My research places individuals and their data at the center of ML privacy, building systems that protect individuals’ privacy without sacrificing performance. I focus on (1) exploring the threat space in ML privacy to inspire targeted protection, (2) analyzing the root cause of privacy leakage from ML models, and (3) developing individualized privacy guarantees that protect data according to individuals’ unique needs while improving privacy-utility trade-offs. My vision is to advance privacy-preserving ML to address the evolving challenges of increasingly complex ML models and systems. As models grow in scale, integrate diverse data modalities, and become embedded in critical societal applications, protecting individual privacy becomes both more urgent but also more challenging. My goal is to create methods that ensure privacy across a broad spectrum of ML applications, while also addressing the interplay between privacy and other trustworthy ML aspects, and aligning technical privacy measures with legal and societal expectations to meet individual rights.
- Scope:
- Online-Ressource
- Note:
- Open Access
- Archivierung/Langzeitarchivierung gewährleistet
- Keywords:
- trustworthy machine learning ; privacy ; GI junior fellow
- Classification:
- Informatik
- Technik
- Sonstiges
- Collection:
- Informatik
- Technik
- Sonstiges
- Copyright:
- CC BY
- Accessibility:
- Free Access
- Title:
- A self-portrayal of GI Junior Fellow Franziska Boenisch: trustworthy machine learning for individuals
- Publication:
-
Berlin: De Gruyter, 2025
- Language:
- English
- Information:
- Abstract: Machine learning (ML) is increasingly deployed in critical domains such as healthcare, finance, and autonomous driving, where the use of sensitive data raises significant privacy challenges. My research places individuals and their data at the center of ML privacy, building systems that protect individuals’ privacy without sacrificing performance. I focus on (1) exploring the threat space in ML privacy to inspire targeted protection, (2) analyzing the root cause of privacy leakage from ML models, and (3) developing individualized privacy guarantees that protect data according to individuals’ unique needs while improving privacy-utility trade-offs. My vision is to advance privacy-preserving ML to address the evolving challenges of increasingly complex ML models and systems. As models grow in scale, integrate diverse data modalities, and become embedded in critical societal applications, protecting individual privacy becomes both more urgent but also more challenging. My goal is to create methods that ensure privacy across a broad spectrum of ML applications, while also addressing the interplay between privacy and other trustworthy ML aspects, and aligning technical privacy measures with legal and societal expectations to meet individual rights.
- Scope:
- Online-Ressource
- Note:
- Open Access
- Archivierung/Langzeitarchivierung gewährleistet
- Keywords:
- trustworthy machine learning ; privacy ; GI junior fellow
- Classification:
- Informatik
- Technik
- Sonstiges
- Collection:
- Informatik
- Technik
- Sonstiges
- Copyright:
- CC BY
- Accessibility:
- Free Access
