# Generalized PTR: User-Friendly Recipes for Data-Adaptive Algorithms with Differential Privacy

arXiv: https://arxiv.org/abs/2301.00301 · HTML: https://mlsanity.com/paper/2301.00301/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 31 references that are in the map.
- First posted on arXiv 31 Dec 2022.

## Builds on (most cited first, 15 of 15)

- [Renyi Differential Privacy](https://mlsanity.com/paper/1702.07476/) (2017)
- [Semi-supervised Knowledge Transfer for Deep Learning from Private Training Data](https://mlsanity.com/paper/1610.05755/) (2016)
- [Scalable Private Learning with PATE](https://mlsanity.com/paper/1802.08908/) (2018)
- [Improving the Gaussian Mechanism for Differential Privacy: Analytical Calibration and Optimal Denoising](https://mlsanity.com/paper/1805.06530/) (2018)
- [Hyperparameter Tuning with Renyi Differential Privacy](https://mlsanity.com/paper/2110.03620/) (2021)
- [Private Selection from Private Candidates](https://mlsanity.com/paper/1811.07971/) (2018)
- [Differential privacy and robust statistics in high dimensions](https://mlsanity.com/paper/2111.06578/) (2021)
- [Privacy for Free: Posterior Sampling and Stochastic Gradient Monte Carlo](https://mlsanity.com/paper/1502.07645/) (2015)
- [Revisiting differentially private linear regression: optimal and adaptive prediction & estimation in unbounded domain](https://mlsanity.com/paper/1803.02596/) (2018)
- [Private Convex Optimization via Exponential Mechanism](https://mlsanity.com/paper/2203.00263/) (2022)
- [Privately Publishable Per-instance Privacy](https://mlsanity.com/paper/2111.02281/) (2021)
- [Individual Differential Privacy: A Utility-Preserving Formulation of Differential Privacy Guarantees](https://mlsanity.com/paper/1612.02298/) (2016)
- [An end-to-end Differentially Private Latent Dirichlet Allocation Using a Spectral Algorithm](https://mlsanity.com/paper/1805.10341/) (2018)
- [Renyi Differential Privacy of Propose-Test-Release and Applications to Private and Robust Machine Learning](https://mlsanity.com/paper/2209.07716/) (2022)
- [Per-instance Differential Privacy](https://mlsanity.com/paper/1707.07708/) (2017)

## Built on by (most cited first, 6 of 6)

- [Differentially Private Next-Token Prediction of Large Language Models](https://mlsanity.com/paper/2403.15638/) (2024)
- [A Randomized Approach for Tight Privacy Accounting](https://mlsanity.com/paper/2304.07927/) (2023)
- [Data-adaptive Differentially Private Prompt Synthesis for In-Context Learning](https://mlsanity.com/paper/2410.12085/) (2024)
- [Adaptively Private Next-Token Prediction of Large Language Models](https://mlsanity.com/paper/2410.02016/) (2024)
- [Privacy Profiles for Private Selection](https://mlsanity.com/paper/2402.06701/) (2024)
- [DPrivBench: Benchmarking LLMs' Reasoning for Differential Privacy](https://mlsanity.com/paper/2604.15851/) (2026)

## Abstract (arXiv)

The ''Propose-Test-Release'' (PTR) framework is a classic recipe for designing differentially private (DP) algorithms that are data-adaptive, i.e. those that add less noise when the input dataset is nice. We extend PTR to a more general setting by privately testing data-dependent privacy losses rather than local sensitivity, hence making it applicable beyond the standard noise-adding mechanisms, e.g. to queries with unbounded or undefined sensitivity. We demonstrate the versatility of generalized PTR using private linear regression as a case study. Additionally, we apply our algorithm to solve an open problem from ''Private Aggregation of Teacher Ensembles (PATE)'' -- privately releasing the entire model with a delicate data-dependent analysis.