Utility-based differentially private recommendation 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

@article{sangeetha2021utility,
  title   = {Utility-based differentially private recommendation system},
  author  = {S Sangeetha and G Sudha Sadasivam and R Latha},
  journal = {Big Data 9(3), 203-218},
  year    = {2021},
  doi     = {10.1089/big.2020.0038},
  url     = {https://www.liebertpub.com/doi/abs/10.1089/big.2020.0038},
}

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