Privacy preserving hybrid recommender system based on deep learning

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

@article{selvaraj2021privacy,
  title   = {Privacy preserving hybrid recommender system based on deep learning},
  author  = {S Selvaraj and SS Gangadharan},
  journal = {Turkish Journal of Electrical Engineering and Computer Sciences 29(5), 2385},
  year    = {2021},
  doi     = {10.3906/elk-2010-40},
  url     = {https://journals.tubitak.gov.tr/elektrik/vol29/iss5/9/},
}

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