One Article Review
| Source |  NIST Security |
|---|---|
| Identifiant | 2103397 |
| Date de publication | 2020-10-29 12:00:00 (vue: 2020-12-15 21:05:58) |
| Titre | Counting Queries: Extracting Key Business Metrics from Datasets |
| Texte | This post is part of a series on differential privacy. Learn more and browse all the posts published to date on the differential privacy blog series page in NIST's Privacy Engineering Collaboration Space. How many people drink pumpkin spice lattes in October, and how would you calculate this without learning specifically who is drinking them, and who is not? While they seem simple or trivial, counting queries are used extremely often. Counting queries such as histograms can express many useful business metrics. How many transactions took place last week? How did this compare to the previous |
| Envoyé | Oui |
| Condensat | all are blog browse business calculate can collaboration compare counting datasets date did differential drink drinking engineering express extracting extremely from histograms how key last lattes learn learning many metrics more nist not october often page part people place post posts previous privacy published pumpkin queries queries: seem series simple space specifically spice such them took transactions trivial used useful week who without would |
| Tags | |
| Stories | |
| Notes | ★★★ |
| Move | 
|
L'article ne semble pas avoir été repris aprés sa publication.
L'article ne semble pas avoir été repris sur un précédent.