arXiv subjects

Jorg Drechsler

Publications and source records attributed to Jorg Drechsler.

2 recordsLinked to original sources

arXiv2025

A Consensus Privacy Metrics Framework for Synthetic Data

Synthetic data generation is one approach for sharing individual-level data. However, to meet legislative requirements, it is necessary to demonstrate that the individuals' privacy is adequately protected. There is no consolidated standard for measuring privacy in synthetic data. Through an expert panel and consensus process, we developed a framework for evaluating privacy in synthetic data. Our findings indicate that current similarity metrics fail to measure identity disclosure, and their use is discouraged. For differentially private synthetic data, a privacy budget other than close to zero was not considered interpretable. There was consensus on the importance of membership and attribute disclosure, both of which involve inferring personal information about an individual without necessarily revealing their identity. The resultant framework provides precise recommendations for metrics that address these types of disclosures effectively. Our findings further present specific opportunities for future research that can help with widespread adoption of synthetic data.

cs.CR↗

arXiv2022

Imputation under Differential Privacy

The literature on differential privacy almost invariably assumes that the data to be analyzed are fully observed. In most practical applications this is an unrealistic assumption. A popular strategy to address this problem is imputation, in which missing values are replaced by estimated values given the observed data. In this paper we evaluate various approaches to answering queries on an imputed dataset in a differentially private manner, as well as discuss trade-offs as to where along the pipeline privacy is considered. We show that if imputation is done without consideration to privacy, the sensitivity of certain queries can increase linearly with the number of incomplete records. On the other hand, for a general class of imputation strategies, these worst case scenarios can be greatly reduced by ensuring privacy already during the imputation stage. We use a simulated dataset to demonstrate these results across a number of imputation schemes (both private and non-private) and examine their impact on the utility of a private query on the data.

cs.DB↗