On the Lift, Related Privacy Measures, and Applications to Privacy–Utility Trade-Offs
This paper investigates lift, the likelihood ratio between the posterior and prior belief about sensitive features in a dataset. Maximum and minimum lifts over sensitive features quantify the adversary’s knowledge gain and should be bounded to protect privacy. We demonstrate that max- and min-lifts...
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| Autori principali: | , , |
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| Natura: | Artigo |
| Lingua: | Inglês |
| Pubblicazione: |
MDPI AG
2023-04-01
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| Serie: | Entropy |
| Soggetti: | |
| Accesso online: | https://www.mdpi.com/1099-4300/25/4/679 |
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