Naturally Private Recommendations with Determinantal Point Processes

• URL: http://arxiv.org/abs/2405.13677v1
• Date: Wed, 22 May 2024 14:20:56 GMT
• Title: Naturally Private Recommendations with Determinantal Point Processes
• Authors: Jack Fitzsimons, Agustín Freitas Pasqualini, Robert Pisarczyk, Dmitrii Usynin,
• Abstract summary: We discuss Determinantal Point Processes (DPPs) which balance recommendations based on both the popularity and the diversity of the content. We conclude by proposing simple alternatives to DPPs which would make them more efficient with respect to their privacy-utility trade-off.
• Score: 0.6249768559720122
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Often we consider machine learning models or statistical analysis methods which we endeavour to alter, by introducing a randomized mechanism, to make the model conform to a differential privacy constraint. However, certain models can often be implicitly differentially private or require significantly fewer alterations. In this work, we discuss Determinantal Point Processes (DPPs) which are dispersion models that balance recommendations based on both the popularity and the diversity of the content. We introduce DPPs, derive and discuss the alternations required for them to satisfy epsilon-Differential Privacy and provide an analysis of their sensitivity. We conclude by proposing simple alternatives to DPPs which would make them more efficient with respect to their privacy-utility trade-off.

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