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Found 8 papers, 5 papers with code
Generating Private Synthetic Data with Genetic Algorithms
We study the problem of efficiently generating differentially private synthetic data that approximate the statistical properties of an underlying sensitive dataset.
Confidence-Ranked Reconstruction of Census Microdata from Published Statistics
Our attacks significantly outperform those that are based only on access to a public distribution or population from which the private dataset $D$ was sampled, demonstrating that they are exploiting information in the aggregate statistics $Q(D)$, and not simply the overall structure of the distribution.
Private Synthetic Data with Hierarchical Structure
Moreover, it has not yet been established how one can generate synthetic data at both the group and individual-level while capturing such statistics.
Learning Language and Multimodal Privacy-Preserving Markers of Mood from Mobile Data
Using computational models, we find that language and multimodal representations of mobile typed text (spanning typed characters, words, keystroke timings, and app usage) are predictive of daily mood.
Iterative Methods for Private Synthetic Data: Unifying Framework and New Methods
We study private synthetic data generation for query release, where the goal is to construct a sanitized version of a sensitive dataset, subject to differential privacy, that approximately preserves the answers to a large collection of statistical queries.
Leveraging Public Data for Practical Private Query Release
In many statistical problems, incorporating priors can significantly improve performance.
Multimodal Privacy-preserving Mood Prediction from Mobile Data: A Preliminary Study
Mental health conditions remain under-diagnosed even in countries with common access to advanced medical care.
Think Locally, Act Globally: Federated Learning with Local and Global Representations
To this end, we propose a new federated learning algorithm that jointly learns compact local representations on each device and a global model across all devices.
