Name
Abstract | ||
|---|---|---|
We identify and investigate a strong connection between probabilistic inference and differential privacy, the latter being a recent privacy definition that permits only indirect observation of data through noisy measurement. Previous research on differential privacy has focused on designing measurement processes whose output is likely to be useful on its own. We consider the potential of applying probabilistic inference to the measurements and measurement process to derive posterior distributions over the data sets and model parameters thereof. We find that probabilistic inference can improve accuracy, integrate multiple observations, measure uncertainty, and even provide posterior distributions over quantities that were not directly measured. |
Year | Venue | Keywords |
|---|---|---|
2010 | NIPS | measurement uncertainty,posterior distribution,machine learning |
Field | DocType | Citations |
Probabilistic inference,Data mining,Frequentist inference,Data set,Differential privacy,Computer science,Fiducial inference,Probabilistic logic network,Artificial intelligence,Probabilistic relevance model,Machine learning | Conference | 10 |
PageRank | References | Authors |
0.72 | 4 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Oliver Williams | 1 | 286 | 18.69 |
| Frank McSherry | 2 | 4289 | 288.94 |