Weerarka Xubinnimada Kediska

Jawaabaha iyo sharraxaadaha waxay soo baxayaan markaad qaadanayso kediska.

1. Waa maxay weerarka xubinnimada ee isku dayaya in la go'aamiyo?
   • Haddii diiwaan gaar ah uu qayb ka ahaa tababarka loo dejiyay
   • saxnaanta moodeelka
   • Nashqada modelka
   • Baaxadda tijaabada tijaabada
2. How can overfitting affect membership-inference exposure in studied models?
   • It proves every training record is identifiable with certainty
   • It removes the need to evaluate attack errors
   • It can increase the behavioral difference between members and comparable nonmembers
   • It makes all outputs independent of training data
3. Waa maxay farsamada sameeyay asalka 2017 Shokri et al. weerarku ku tiirsan yahay?
   • Goynta si tartiib ah
   • Moodooyinka hadhku
   • Baadhitaanka Beam
   • Calaamadaynta
4. Maxay tahay sababta ka-qaybgalka xubinnimadu u noqon karto waxyeello xitaa iyada oo aan la shaacin waxa diiwaanka ku jira?
   • Waxay hoos u dhigtaa qaabka
   • Waxay beddeshaa miisaanka modelka
   • Waxay tirtirtaa xogta tababarka
   • Xubinimada lafteedu waxay daaha ka qaadi kartaa xaqiiqooyin xasaasi ah, sida ogaanshaha cudurka
5. What calibration distinguishes the cited LiRA approach from a single naive loss threshold?
   • A guarantee that every record has the same inherent difficulty
   • Only an increased generation temperature
   • Per-example score distributions estimated from in and out models
   • A requirement that the target reveal its entire training dataset
6. What does a properly specified differential-privacy guarantee bound?
   • Changes in output probabilities between neighboring datasets
   • A guarantee that no attack can ever make any correct guess
   • A mandatory deletion of all model outputs
   • Only the raw number of files in a training folder
7. An attack incorrectly flags 10 of 1,000 actual nonmembers as members. What is its false-positive rate on those nonmembers?
   • 10%
   • 0.1%
   • 100%
   • 1%
8. Why does the LiRA paper emphasize true-positive rate at low false-positive rates?
   • False positives are impossible by definition
   • Average accuracy can conceal whether confidently flagged records are reliable membership evidence
   • Every member and nonmember population must have identical sizes
   • The model’s training accuracy directly gives attack precision