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Found 6 papers, 2 papers with code
Initial Exploration of Zero-Shot Privacy Utility Tradeoffs in Tabular Data Using GPT-4
Our approach entails prompting GPT-4 by transforming tabular data points into textual format, followed by the inclusion of precise sanitization instructions in a zero-shot manner.
Optimizing Privacy and Utility Tradeoffs for Group Interests Through Harmonization
Unlike previous studies that primarily focus on scenarios where all users share identical private and utility attributes and often rely on auxiliary datasets or manual annotations, we introduce a collaborative data-sharing mechanism between two user groups through a trusted third party.
A Comprehensive and Reliable Feature Attribution Method: Double-sided Remove and Reconstruct (DoRaR)
While avoiding the artifacts problem, this new category suffers from the Encoding Prediction in the Explanation (EPITE) problem, in which the predictor's decisions rely not on the features, but on the masks that selects those features.
Uncertainty-Autoencoder-Based Privacy and Utility Preserving Data Type Conscious Transformation
We propose an adversarial learning framework that deals with the privacy-utility tradeoff problem under two types of conditions: data-type ignorant, and data-type aware.
UAE-PUPET: An Uncertainty-Autoencoder-Based Privacy and Utility Preserving End-to-End Transformation
The dynamic setting corresponds to the min-max two-player game whereas the constant setting corresponds to a generator which tries to outperform an adversary already trained using ground truth data.
An Information Diffusion Approach to Rumor Propagation and Identification on Twitter
We show that the models for True and False message propagation differ significantly, both in the prediction parameters and in the message features that govern the diffusion.
