"Evaluating the Impact of Data Anonymization in a Machine Learning Application."

Lelio Campanile et al. (2022)

> Home

Details and statistics

DOI: 10.1007/978-3-031-10542-5_27

access: closed

type: Conference or Workshop Paper

metadata version: 2022-10-02

- view
  - electronic edition via DOI
  - unpaywalled version
  authority control:
- export record
  dblp key:
  - conf/iccsa/CampanileFMPS50
- ask others
- share record
  persistent URL:
  - https://dblp.org/rec/conf/iccsa/CampanileFMPS50
Lelio Campanile, Fabio Forgione, Michele Mastroianni, Gianfranco Palmiero, Carlo Sanghez:
Evaluating the Impact of Data Anonymization in a Machine Learning Application. ICCSA (Workshops 4) 2022: 389-400

manage site settings

To protect your privacy, all features that rely on external API calls from your browser are turned off by default. You need to opt-in for them to become active. All settings here will be stored as cookies with your web browser. For more information see our F.A.Q.