# Randomized algorithms for precise measurement of differentially-private, personalized recommendations

arXiv: https://arxiv.org/abs/2308.03735 · HTML: https://mlsanity.com/paper/2308.03735/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 58 references that are in the map.
- First posted on arXiv 7 Aug 2023.

## Builds on (most cited first, 14 of 14)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Deep Learning with Differential Privacy](https://mlsanity.com/paper/1607.00133/) (2016)
- [Wide & Deep Learning for Recommender Systems](https://mlsanity.com/paper/1606.07792/) (2016)
- [Deep Interest Network for Click-Through Rate Prediction](https://mlsanity.com/paper/1706.06978/) (2017)
- [RAPPOR: Randomized Aggregatable Privacy-Preserving Ordinal Response](https://mlsanity.com/paper/1407.6981/) (2014)
- [Amplification by Shuffling: From Local to Central Differential Privacy via Anonymity](https://mlsanity.com/paper/1811.12469/) (2018)
- [Differentially Private Contextual Linear Bandits](https://mlsanity.com/paper/1810.00068/) (2018)
- [Prio: Private, Robust, and Scalable Computation of Aggregate Statistics](https://mlsanity.com/paper/1703.06255/) (2017)
- [Permute-and-Flip: A new mechanism for differentially private selection](https://mlsanity.com/paper/2010.12603/) (2020)
- [Learning Piece-wise Linear Models from Large Scale Data for Ad Click Prediction](https://mlsanity.com/paper/1704.05194/) (2017)
- [The Permute-and-Flip Mechanism is Identical to Report-Noisy-Max with Exponential Noise](https://mlsanity.com/paper/2105.07260/) (2021)
- [An Efficient Deep Distribution Network for Bid Shading in First-Price Auctions](https://mlsanity.com/paper/2107.06650/) (2021)
- [Bid Shading by Win-Rate Estimation and Surplus Maximization](https://mlsanity.com/paper/2009.09259/) (2020)
- [Private Federated Statistics in an Interactive Setting](https://mlsanity.com/paper/2211.10082/) (2022)

## Built on by (most cited first, 1 of 1)

- [Private Selection with Heterogeneous Sensitivities](https://mlsanity.com/paper/2501.05309/) (2025)

## Abstract (arXiv)

Personalized recommendations form an important part of today's internet ecosystem, helping artists and creators to reach interested users, and helping users to discover new and engaging content. However, many users today are skeptical of platforms that personalize recommendations, in part due to historically careless treatment of personal data and data privacy. Now, businesses that rely on personalized recommendations are entering a new paradigm, where many of their systems must be overhauled to be privacy-first. In this article, we propose an algorithm for personalized recommendations that facilitates both precise and differentially-private measurement. We consider advertising as an example application, and conduct offline experiments to quantify how the proposed privacy-preserving algorithm affects key metrics related to user experience, advertiser value, and platform revenue compared to the extremes of both (private) non-personalized and non-private, personalized implementations.