Privacy preserving bloom recommender system

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{selvaraj2021privacy,
  title   = {Privacy preserving bloom recommender system},
  author  = {S Selvaraj and GS Sadasivam and DT Goutham and A Srikanth and J Vinith},
  booktitle = {2021 International Conference on Computer Communication and Informatics},
  year    = {2021},
  doi     = {10.1109/ICCCI50826.2021.9402528},
  url     = {https://ieeexplore.ieee.org/abstract/document/9402528},
}

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