Confluence of Cryptography and Differential Privacy: A Hybrid Approach for Privacy Preserving Collaborative Filtering

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{sangeetha2022confluence,
  title   = {Confluence of Cryptography and Differential Privacy: A Hybrid Approach for Privacy Preserving Collaborative Filtering},
  author  = {S Sangeetha and G Sudha Sadasivam and V Nithesh and K Mounish},
  booktitle = {Proceedings of the International Conference on Paradigms of Communication},
  year    = {2022},
  doi     = {10.1007/978-981-16-5747-4_29},
  url     = {https://link.springer.com/chapter/10.1007/978-981-16-5747-4_29},
}

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