Elevating privacy: A differential privacy infused approach to GAN for robust data synthesis for deep learning models

The question behind it

Can a recommender learn what you like without learning who you are?

The core of my doctoral work. Differential privacy adds calibrated noise so that no single person can be reverse-engineered out of a model. The catch is that noise costs accuracy, and these papers measure that trade-off and find where a recommender stays useful.

A plain-language summary of this paper is on its way here. The full text will follow wherever the publisher permits me to host it.

Cite this paper

@inproceedings{sangeetha2024elevating,
  title   = {Elevating privacy: A differential privacy infused approach to GAN for robust data synthesis for deep learning models},
  author  = {S Sangeetha and E Shriaarthy and N Rithvik Pranao and JMS Priya and L Yogeswari and et al.},
  booktitle = {Advanced Computing and Communications Conference, pp. 267-278},
  year    = {2024},
  doi     = {10.1007/978-3-031-96473-2_19},
  url     = {https://doi.org/10.1007/978-3-031-96473-2_19},
}

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