Questionário sobre ataques de inferência de membros

As respostas e explicações aparecem conforme você responde ao teste.

1. O que um ataque de inferência de associação está tentando determinar?
   • Se um registro específico fazia parte do conjunto de treinamento
   • A precisão do modelo
   • A arquitetura do modelo
   • O tamanho do conjunto de teste
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. Que técnica o Shokri et al. original de 2017 utilizou? ataque confiar?
   • Recorte gradiente
   • Modelos de sombra
   • Pesquisa de feixe
   • Tokenização
4. Por que a inferência de adesão pode ser prejudicial mesmo sem revelar o conteúdo do registro?
   • Isso retarda o modelo
   • Muda os pesos do modelo
   • Exclui dados de treinamento
   • A própria adesão pode revelar fatos delicados, como um diagnóstico
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