# Blowfish Privacy: Tuning Privacy-Utility Trade-offs using Policies

arXiv: https://arxiv.org/abs/1312.3913 · HTML: https://mlsanity.com/paper/1312.3913/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Dec 2013.

## Built on by (most cited first, 12 of 12)

- [OpBoost: A Vertical Federated Tree Boosting Framework Based on Order-Preserving Desensitization](https://mlsanity.com/paper/2210.01318/) (2022)
- [Algorithms with More Granular Differential Privacy Guarantees](https://mlsanity.com/paper/2209.04053/) (2022)
- [Tree-based Models for Vertical Federated Learning: A Survey](https://mlsanity.com/paper/2504.02285/) (2025)
- [R'enyi Pufferfish Privacy: General Additive Noise Mechanisms and Privacy Amplification by Iteration](https://mlsanity.com/paper/2312.13985/) (2023)
- [Enhancing Feature-Specific Data Protection via Bayesian Coordinate Differential Privacy](https://mlsanity.com/paper/2410.18404/) (2024)
- [On Privacy and Confidentiality of Communications in Organizational Graphs](https://mlsanity.com/paper/2105.13418/) (2021)
- [A Shuffling Framework for Local Differential Privacy](https://mlsanity.com/paper/2106.06603/) (2021)
- [Rainbow Differential Privacy](https://mlsanity.com/paper/2202.03974/) (2022)
- [Power Mechanism: Private Tabular Representation Release for Model Agnostic Consumption](https://mlsanity.com/paper/2510.05581/) (2025)
- [Cross-Silo De-Anonymization Under Local Differential Privacy: Threat Model, Phase Transition, and Coordination Necessity](https://mlsanity.com/paper/2606.16763/) (2026)
- [Predictability as a Fine-Grained Measure for Privacy](https://mlsanity.com/paper/2606.20546/) (2026)
- [Agents That Know Too Much: A Data-Centric Survey of Privacy in LLM Agents](https://mlsanity.com/paper/2606.26627/) (2026)

## Abstract (arXiv)

Privacy definitions provide ways for trading-off the privacy of individuals in a statistical database for the utility of downstream analysis of the data. In this paper, we present Blowfish, a class of privacy definitions inspired by the Pufferfish framework, that provides a rich interface for this trade-off. In particular, we allow data publishers to extend differential privacy using a policy, which specifies (a) secrets, or information that must be kept secret, and (b) constraints that may be known about the data. While the secret specification allows increased utility by lessening protection for certain individual properties, the constraint specification provides added protection against an adversary who knows correlations in the data (arising from constraints). We formalize policies and present novel algorithms that can handle general specifications of sensitive information and certain count constraints. We show that there are reasonable policies under which our privacy mechanisms for k-means clustering, histograms and range queries introduce significantly lesser noise than their differentially private counterparts. We quantify the privacy-utility trade-offs for various policies analytically and empirically on real datasets.