# Building a RAPPOR with the Unknown: Privacy-Preserving Learning of Associations and Data Dictionaries

arXiv: https://arxiv.org/abs/1503.01214 · HTML: https://mlsanity.com/paper/1503.01214/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Mar 2015.

## Built on by (most cited first, 16 of 16)

- [Robust and Differentially Private Mean Estimation](https://mlsanity.com/paper/2102.09159/) (2021)
- [DP-PCA: Statistically Optimal and Differentially Private PCA](https://mlsanity.com/paper/2205.13709/) (2022)
- [How unfair is private learning ?](https://mlsanity.com/paper/2206.03985/) (2022)
- [Fair NLP Models with Differentially Private Text Encoders](https://mlsanity.com/paper/2205.06135/) (2022)
- [Near Optimal Private and Robust Linear Regression](https://mlsanity.com/paper/2301.13273/) (2023)
- [Federated Analytics: A survey](https://mlsanity.com/paper/2302.01326/) (2023)
- [Private Vector Mean Estimation in the Shuffle Model: Optimal Rates Require Many Messages](https://mlsanity.com/paper/2404.10201/) (2024)
- [Locally Differentially Private Bayesian Inference](https://mlsanity.com/paper/2110.14426/) (2021)
- [One-bit Submission for Locally Private Quasi-MLE: Its Asymptotic Normality and Limitation](https://mlsanity.com/paper/2202.07194/) (2022)
- [On the Utility Gain of Iterative Bayesian Update for Locally Differentially Private Mechanisms](https://mlsanity.com/paper/2307.07744/) (2023)
- [Unleash the Power of Ellipsis: Accuracy-enhanced Sparse Vector Technique with Exponential Noise](https://mlsanity.com/paper/2407.20068/) (2024)
- [PREAMBLE: Private and Efficient Aggregation via Block Sparse Vectors](https://mlsanity.com/paper/2503.11897/) (2025)
- [BinaryShield: Cross-Service Threat Intelligence in LLM Services using Privacy-Preserving Fingerprints](https://mlsanity.com/paper/2509.05608/) (2025)
- [A Shuffling Framework for Local Differential Privacy](https://mlsanity.com/paper/2106.06603/) (2021)
- [Momentum Gradient Descent Federated Learning with Local Differential Privacy](https://mlsanity.com/paper/2209.14086/) (2022)
- [ABC: Numerical Data Collection under Local Differential Privacy without Prior Knowledge](https://mlsanity.com/paper/2608.05737/) (2026)

## Abstract (arXiv)

Techniques based on randomized response enable the collection of potentially sensitive data from clients in a privacy-preserving manner with strong local differential privacy guarantees. One of the latest such technologies, RAPPOR, allows the marginal frequencies of an arbitrary set of strings to be estimated via privacy-preserving crowdsourcing. However, this original estimation process requires a known set of possible strings; in practice, this dictionary can often be extremely large and sometimes completely unknown. In this paper, we propose a novel decoding algorithm for the RAPPOR mechanism that enables the estimation of "unknown unknowns," i.e., strings we do not even know we should be estimating. To enable learning without explicit knowledge of the dictionary, we develop methodology for estimating the joint distribution of two or more variables collected with RAPPOR. This is a critical step towards understanding relationships between multiple variables collected in a privacy-preserving manner.