Adjustable Privacy using Autoencoder-based Learning Structure
This addresses privacy concerns for data providers in inference centers, offering an incremental improvement over existing methods.
The paper tackles the privacy-utility trade-off in data sharing by proposing an autoencoder-based method that separates and protects confidential features while allowing adjustable privacy levels, achieving significant performance improvements on image and categorical datasets.
Inference centers need more data to have a more comprehensive and beneficial learning model, and for this purpose, they need to collect data from data providers. On the other hand, data providers are cautious about delivering their datasets to inference centers in terms of privacy considerations. In this paper, by modifying the structure of the autoencoder, we present a method that manages the utility-privacy trade-off well. To be more precise, the data is first compressed using the encoder, then confidential and non-confidential features are separated and uncorrelated using the classifier. The confidential feature is appropriately combined with noise, and the non-confidential feature is enhanced, and at the end, data with the original data format is produced by the decoder. The proposed architecture also allows data providers to set the level of privacy required for confidential features. The proposed method has been examined for both image and categorical databases, and the results show a significant performance improvement compared to previous methods.