Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
121 tokens/sec
GPT-4o
9 tokens/sec
Gemini 2.5 Pro Pro
47 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
38 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

Model-based Differentially Private Data Synthesis and Statistical Inference in Multiply Synthetic Differentially Private Data (1606.08052v3)

Published 26 Jun 2016 in stat.ME

Abstract: We propose the approach of model-based differentially private synthesis (modips) in the Bayesian framework for releasing individual-level surrogate/synthetic datasets with privacy guarantees given the original data. The modips technique integrates the concept of differential privacy into model-based data synthesis. We introduce several variants for the general modips approach and different procedures to obtaining privacy-preserving posterior samples, a key step in modips. The uncertainty from the sanitization and synthetic process in modips can be accounted for by releasing multiple synthetic datasets and quantified via an inferential combination rule that is proposed in this paper. We run empirical studies to examine the impacts of the number of synthetic sets and the privacy budget allocation schemes on the inference based on synthetic data.

Summary

We haven't generated a summary for this paper yet.