# Heterogeneous Differential Privacy

arXiv: https://arxiv.org/abs/1504.06998 · HTML: https://mlsanity.com/paper/1504.06998/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Apr 2015.

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

- [Private Adaptive Optimization with Side Information](https://mlsanity.com/paper/2202.05963/) (2022)
- [On Privacy and Personalization in Cross-Silo Federated Learning](https://mlsanity.com/paper/2206.07902/) (2022)
- [Optimal and Differentially Private Data Acquisition: Central and Local Mechanisms](https://mlsanity.com/paper/2201.03968/) (2022)
- [CAPE: Context-Aware Private Embeddings for Private Language Learning](https://mlsanity.com/paper/2108.12318/) (2021)
- [Have it your way: Individualized Privacy Assignment for DP-SGD](https://mlsanity.com/paper/2303.17046/) (2023)
- [Federated Intrusion Detection for IoT with Heterogeneous Cohort Privacy](https://mlsanity.com/paper/2101.09878/) (2021)
- [Individualized PATE: Differentially Private Machine Learning with Individual Privacy Guarantees](https://mlsanity.com/paper/2202.10517/) (2022)
- [Combining Public and Private Data](https://mlsanity.com/paper/2111.00115/) (2021)
- [Noise-Aware Algorithm for Heterogeneous Differentially Private Federated Learning](https://mlsanity.com/paper/2406.03519/) (2024)
- [Mean Estimation Under Heterogeneous Privacy: Some Privacy Can Be Free](https://mlsanity.com/paper/2305.09668/) (2023)
- [Mean Estimation Under Heterogeneous Privacy Demands](https://mlsanity.com/paper/2310.13137/) (2023)
- [Federated Learning with Heterogeneous Differential Privacy](https://mlsanity.com/paper/2110.15252/) (2021)
- [Differentially Private Federated Learning With Time-Adaptive Privacy Spending](https://mlsanity.com/paper/2502.18706/) (2025)
- [Striking a Balance: An Optimal Mechanism Design for Heterogenous Differentially Private Data Acquisition for Logistic Regression](https://mlsanity.com/paper/2309.10340/) (2023)
- [Personalized Differential Privacy for Ridge Regression](https://mlsanity.com/paper/2401.17127/) (2024)
- [Personalized DP-SGD using Sampling Mechanisms](https://mlsanity.com/paper/2305.15165/) (2023)
- [Private Estimation when Data and Privacy Demands are Correlated](https://mlsanity.com/paper/2407.11274/) (2024)
- [Managing Correlations in Data and Privacy Demand](https://mlsanity.com/paper/2509.02856/) (2025)
- [Federated Learning With Individualized Privacy Through Client Sampling](https://mlsanity.com/paper/2501.17634/) (2025)
- [Optimal Client Sampling in Federated Learning with Client-Level Heterogeneous Differential Privacy](https://mlsanity.com/paper/2505.13655/) (2025)
- [Inclusive Federated Learning Through Compliance-Weighted Noise Allocation in Healthcare AI](https://mlsanity.com/paper/2505.22108/) (2025)
- [High-Probability Bounds For Heterogeneous Local Differential Privacy](https://mlsanity.com/paper/2510.11895/) (2025)
- [Partial sensitivity analysis in differential privacy](https://mlsanity.com/paper/2109.10582/) (2021)
- [Decentralized Matrix Factorization with Heterogeneous Differential Privacy](https://mlsanity.com/paper/2212.00306/) (2022)
- [Personalized Privacy Amplification via Importance Sampling](https://mlsanity.com/paper/2307.10187/) (2023)
- [Optimizing Privacy and Utility Tradeoffs for Group Interests Through Harmonization](https://mlsanity.com/paper/2404.05043/) (2024)
- [Controlled privacy leakage propagation throughout overlapping grouped learning](https://mlsanity.com/paper/2503.04054/) (2025)
- [Assessing Per-Sample Membership Inference Vulnerability without Retraining](https://mlsanity.com/paper/2602.15919/) (2026)
- [Limits of Personalizing Differential Privacy Budgets](https://mlsanity.com/paper/2605.13503/) (2026)
- [Jacobian-Guided Anisotropic Noise Reshaping for Enhancing Representation Utility under Local Differential Privacy](https://mlsanity.com/paper/2605.16812/) (2026)

## Abstract (arXiv)

The massive collection of personal data by personalization systems has rendered the preservation of privacy of individuals more and more difficult. Most of the proposed approaches to preserve privacy in personalization systems usually address this issue uniformly across users, thus ignoring the fact that users have different privacy attitudes and expectations (even among their own personal data). In this paper, we propose to account for this non-uniformity of privacy expectations by introducing the concept of heterogeneous differential privacy. This notion captures both the variation of privacy expectations among users as well as across different pieces of information related to the same user. We also describe an explicit mechanism achieving heterogeneous differential privacy, which is a modification of the Laplacian mechanism by Dwork, McSherry, Nissim, and Smith. In a nutshell, this mechanism achieves heterogeneous differential privacy by manipulating the sensitivity of the function using a linear transformation on the input domain. Finally, we evaluate on real datasets the impact of the proposed mechanism with respect to a semantic clustering task. The results of our experiments demonstrate that heterogeneous differential privacy can account for different privacy attitudes while sustaining a good level of utility as measured by the recall for the semantic clustering task.