Towards Effective Measurement of Membership Privacy Risk for Machine Learning Models
- - =#n/a
- - Archaeology And History
- - Biology And Life Sciences
- - Botanical And Agricultural Sciences
- - Business, Management And Economics
- - Chemistry And Chemical Sciences
- - Civil And Structural Engineering
- - Clinical And Medical Sciences
- - Dietetics And Food Science
- - Earth And Planetary Sciences
- - Electrical, Electronics And Communication Engineering
- - Energy And Environmental Science
- - Information And Computing Technologies
- - Legal, Crime, And Investigation Studies
- - Linguistics, Language And Literature
- - Mass Media And Journalism
- - Mathematical And Statistical Sciences
- - Mechanical Engineering And Applied Mechanics
- - Pedagogical And Educational Research
- - Pharmacy And Pharmaceutical Sciences
- - Philosophical Studies
- - Physics And Natural Sciences
- - Politics And Government
- - Psychology And Cognitive Sciences
- - Sociology And Social Research
Book Details :
Towards Effective Measurement of Membership Privacy Risk for Machine Learning Models