# Differential Privacy: An Economic Method for Choosing Epsilon

arXiv: https://arxiv.org/abs/1402.3329 · HTML: https://mlsanity.com/paper/1402.3329/

## Key facts

- Cited by 35 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Feb 2014.

## Built on by (most cited first, 30 of 35)

- [Synthetic Text Generation with Differential Privacy: A Simple and Practical Recipe](https://mlsanity.com/paper/2210.14348/) (2022)
- [Synthetic Data -- what, why and how?](https://mlsanity.com/paper/2205.03257/) (2022)
- [On Memorization in Probabilistic Deep Generative Models](https://mlsanity.com/paper/2106.03216/) (2021)
- [DP-BART for Privatized Text Rewriting under Local Differential Privacy](https://mlsanity.com/paper/2302.07636/) (2023)
- [Optimal and Differentially Private Data Acquisition: Central and Local Mechanisms](https://mlsanity.com/paper/2201.03968/) (2022)
- [Privacy-Preserving Graph Convolutional Networks for Text Classification](https://mlsanity.com/paper/2102.09604/) (2021)
- [The Inadequacy of Similarity-based Privacy Metrics: Privacy Attacks against "Truly Anonymous" Synthetic Datasets](https://mlsanity.com/paper/2312.05114/) (2023)
- [LDP-Feat: Image Features with Local Differential Privacy](https://mlsanity.com/paper/2308.11223/) (2023)
- [Attack-Aware Noise Calibration for Differential Privacy](https://mlsanity.com/paper/2407.02191/) (2024)
- [DP-NMT: Scalable Differentially-Private Machine Translation](https://mlsanity.com/paper/2311.14465/) (2023)
- [Federated Calibration and Evaluation of Binary Classifiers](https://mlsanity.com/paper/2210.12526/) (2022)
- [Differential Privacy with Random Projections and Sign Random Projections](https://mlsanity.com/paper/2306.01751/) (2023)
- [Protect Your Score: Contact Tracing With Differential Privacy Guarantees](https://mlsanity.com/paper/2312.11581/) (2023)
- [Crowding Out The Noise: Algorithmic Collective Action Under Differential Privacy](https://mlsanity.com/paper/2505.05707/) (2025)
- [Probing the Transition to Dataset-Level Privacy in ML Models Using an Output-Specific and Data-Resolved Privacy Profile](https://mlsanity.com/paper/2306.15790/) (2023)
- [When Synthetic Data Met Regulation](https://mlsanity.com/paper/2307.00359/) (2023)
- [Quantifying identifiability to choose and audit ε in differentially private deep learning](https://mlsanity.com/paper/2103.02913/) (2021)
- [Lightweight Transformer in Federated Setting for Human Activity Recognition](https://mlsanity.com/paper/2110.00244/) (2021)
- [Differential Privacy in Cooperative Multiagent Planning](https://mlsanity.com/paper/2301.08811/) (2023)
- [Defining 'Good': Evaluation Framework for Synthetic Smart Meter Data](https://mlsanity.com/paper/2407.11785/) (2024)
- [On the Challenges of Deploying Privacy-Preserving Synthetic Data in the Enterprise](https://mlsanity.com/paper/2307.04208/) (2023)
- [Evaluating the Impact of Local Differential Privacy on Utility Loss via Influence Functions](https://mlsanity.com/paper/2309.08678/) (2023)
- [NoEsis: Differentially Private Knowledge Transfer in Modular LLM Adaptation](https://mlsanity.com/paper/2504.18147/) (2025)
- [BinaryShield: Cross-Service Threat Intelligence in LLM Services using Privacy-Preserving Fingerprints](https://mlsanity.com/paper/2509.05608/) (2025)
- [Online Linear Regression with Paid Stochastic Features](https://mlsanity.com/paper/2511.08073/) (2025)
- [Towards Practical Differential Privacy in Data Analysis: Understanding the Effect of Epsilon on Utility in Private ERM](https://mlsanity.com/paper/2206.03488/) (2022)
- [Data Analytics with Differential Privacy](https://mlsanity.com/paper/2311.16104/) (2023)
- [DNA: Differentially private Neural Augmentation for contact tracing](https://mlsanity.com/paper/2404.13381/) (2024)
- [Learning to Diagnose Privately: DP-Powered LLMs for Radiology Report Classification](https://mlsanity.com/paper/2506.04450/) (2025)
- [Blind Targeting: Personalization under Third-Party Privacy Constraints](https://mlsanity.com/paper/2507.05175/) (2025)

## Abstract (arXiv)

Differential privacy is becoming a gold standard for privacy research; it offers a guaranteed bound on loss of privacy due to release of query results, even under worst-case assumptions. The theory of differential privacy is an active research area, and there are now differentially private algorithms for a wide range of interesting problems. However, the question of when differential privacy works in practice has received relatively little attention. In particular, there is still no rigorous method for choosing the key parameter ε, which controls the crucial tradeoff between the strength of the privacy guarantee and the accuracy of the published results. In this paper, we examine the role that these parameters play in concrete applications, identifying the key questions that must be addressed when choosing specific values. This choice requires balancing the interests of two different parties: the data analyst and the prospective participant, who must decide whether to allow their data to be included in the analysis. We propose a simple model that expresses this balance as formulas over a handful of parameters, and we use our model to choose ε on a series of simple statistical studies. We also explore a surprising insight: in some circumstances, a differentially private study can be more accurate than a non-private study for the same cost, under our model. Finally, we discuss the simplifying assumptions in our model and outline a research agenda for possible refinements.