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Kuiz Privasi Berbeza

Amalkan kepekaan privasi yang berbeza, komposisi dan DP-SGD, termasuk sebab had kehilangan privasi formal berbeza daripada janji tanpa hafalan.

Laluan panduan yang berkaitanPrivasi Berbeza
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For otherwise comparable differential-privacy mechanisms, what does a smaller epsilon specify?

Pratonton setiap soalan8 soalan

Jawapan dan penjelasan muncul semasa anda mengambil kuiz.

  1. For otherwise comparable differential-privacy mechanisms, what does a smaller epsilon specify?
    • A larger dataset file
    • An increased model parameter count
    • A tighter bound on the allowed privacy loss
    • A faster GPU clock
  2. Why is noise calibrated to sensitivity in a differential-privacy mechanism?
    • To encrypt every raw record
    • To limit how a permitted neighboring-data change affects released output distributions
    • To remove the need to define neighboring datasets
    • To guarantee every person is impossible to identify under all conditions
  3. Apakah yang membezakan model 'tempatan' bagi privasi pembezaan daripada model 'pusat'?
    • Tempatan memerlukan kurator pusat yang dipercayai sepenuhnya
    • Tempatan tidak pernah menggunakan sebarang bunyi bising sama sekali
    • Tempatan berfungsi hanya pada imej
    • Setempat menambah hingar pada peranti setiap orang sebelum data keluar
  4. Apakah 'sensitiviti' digunakan semasa menentukur bunyi?
    • Mengukur berapa banyak data seseorang boleh mengubah hasil pertanyaan
    • Mengira bilangan lajur dalam jadual
    • Mengesan suhu perkakasan
    • Memilih fon untuk laporan
  5. Mengapakah setiap pertanyaan baharu dibelanjakan daripada 'belanjawan privasi'?
    • Pertanyaan memerlukan wang setiap baris
    • Kehilangan privasi terkumpul merentas pelbagai analisis
    • Set data mengecut dengan setiap pertanyaan
    • Belanjawan hanya mengenai had storan
  6. What does correctly configured DP-SGD aim to bound through clipping, noise, and privacy accounting?
    • Every possibility of memorizing any fact
    • The total number of model layers automatically
    • The need for a training objective
    • The privacy loss associated with the specified neighboring training datasets
  7. A count query includes each person at most once. Under adding or removing one person, what is its sensitivity?
    • satu
    • The entire number of database columns
    • Zero because it is a count
    • The model’s hidden dimension
  8. Two separately randomized analyses use the same private dataset. Why evaluate them together?
    • The second analysis deletes the first output
    • Each analysis makes the previous privacy cost exactly zero
    • Privacy loss can compose across the released outputs
    • Composition applies only to public data