Microsimulations are used as a tool for evidence-based policy, to better understand the impact of policies on society. However, the quality of the outcome considerably depends on the quality of the data in use. In order to provide a rich set of variables on granular details, synthetic data may be involved. This becomes even more important in dynamic microsimulation where projection into the future is simulated or when providing an open data environment for the research community with a vast amount of variables including geocoded information. The present paper discusses opportunities and challenges of such synthetic but realistic data generation in a microsimulation data lab, which includes two steps of disclosure control. First, the base information which is used for the synthetic data generation has to be reviewed in terms of disclosure risks. Second, the data generating process must be ensured to not systematically reproduce rare events for (synthetic) individuals or replicating original input data. Otherwise, due to the large amount of additional information, this may lead to a cumulative effect of the individual re-identification risks. In order to make these output data of dynamic spatial microsimulations available in the sense of open and reproducible research, such statistical disclosure risks must be excluded a priori. This study examines whether disclosure risks may occur when synthetic data is generated via anonymized data from official statistics sources and how these can be avoided in principle. Furthermore, we discuss methods within the framework of spatial dynamic microsimulation frameworks that automatically ensure the standards of statistical disclosure control as well as official statistics data providers during simulation runs.
This paper studies attribute disclosure risk in aggregated smart meter data. Smart meter data is commonly aggregated to preserve the privacy of individual contributions. The published data shows aggregated consumption, preventing the revelation of individual consumption patterns. There is, however, a potential risk associated to aggregated data. We analyze some datasets of smart meter data consumption to show the potential risk of attribute disclosure. We observe that, even if data is aggregated with the most favorable aggregation approach, it presents this attribute disclosure risk.
It is possible to produce differentially private k-anonymized data based on the method of random sampling followed by full-domain generalization for k-anonymization. We previously evaluate the performance of that method, which is implemented as the SafePub algorithm in the ARX anonymization tool. However, since the SafePub algorithm uses the maximum sampling rate that satisfies the requirements for differential privacy, we observe paradoxical results where data utility diminishes as the privacy budget for differential privacy increases. In this paper, we, therefore, conduct preliminary experiments to explore the parameter space for privacy budget and sampling rate by setting the sampling rates explicitly through modifications to the implementation of ARX. Our initial results show the possibility of improving the utility of anonymized data by properly setting the sampling rate below its maximum value.
Explainable AI (XAI) metrics have gained attention because of a need to ensure fairness and transparency in machine learning models by providing users with some understanding of the models' internal processes. Many services, including Amazon Web Services, the Google Cloud Platform, and Microsoft Azure run machine-learning-as-a-service platforms, which provide several indices, including Shapley values, that explain the relationship between the output of the black-box model and its private input features. However, in 2022, it was demonstrated that a Shapley-value-based explanation could lead to the reconstruction of private attributes, posing a privacy risk of information leakage from the model. It was shown that the leaked value would depend on the AI black-box model used. However, it was not clear which combinations of black-box model and XAI metric would be vulnerable to a reconstruction attack. The present study shows, both theoretically and experimentally, that Shapley values are indeed vulnerable to a reconstruction attack. We prove that Shapley values for a linear model can lead to a perfect reconstruction of records, that is, they can enable an accurate estimation of private values. In addition, we investigate the impact of various optimization algorithms used in attack models on the reconstruction risk.
Quantile regression (QR) is a powerful and robust statistical modeling method broadly used in many fields such as economics, ecology, and healthcare. However, it has not been well-explored in differential privacy (DP) since its loss function lacks strong convexity and twice differentiability, often required by many DP mechanisms. We implement the smoothed QR loss via convolution within the K-Norm Gradient mechanism (KNG) and prove the resulting estimate converges to the non-private one asymptotically. Additionally, our work is the first to extensively investigate the empirical performance of DP smoothing QR under pure-, approximate- and concentrated-DP and four mechanisms, and cases commonly encountered in practice such as heavy-tailed and heteroscedastic data. We find that the Objective Perturbation Mechanism and KNG are the top performers across the simulated settings.
