# SLDP: Semi-Local Differential Privacy for Density-Adaptive Analytics

arXiv: https://arxiv.org/abs/2602.18910 · HTML: https://mlsanity.com/paper/2602.18910/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 34 references that are in the map.
- First posted on arXiv 21 Feb 2026.

## Builds on (most cited first, 13 of 13)

- [The Composition Theorem for Differential Privacy](https://mlsanity.com/paper/1311.0776/) (2013)
- [Amplification by Shuffling: From Local to Central Differential Privacy via Anonymity](https://mlsanity.com/paper/1811.12469/) (2018)
- [How to DP-fy ML: A Practical Guide to Machine Learning with Differential Privacy](https://mlsanity.com/paper/2303.00654/) (2023)
- [PrivTree: A Differentially Private Algorithm for Hierarchical Decompositions](https://mlsanity.com/paper/1601.03229/) (2016)
- [Decision Tree Classification with Differential Privacy: A Survey](https://mlsanity.com/paper/1611.01919/) (2016)
- [The Role of Interactivity in Local Differential Privacy](https://mlsanity.com/paper/1904.03564/) (2019)
- [Local differential privacy: Elbow effect in optimal density estimation and adaptation over Besov ellipsoids](https://mlsanity.com/paper/1903.01927/) (2019)
- [Private High-Dimensional Hypothesis Testing](https://mlsanity.com/paper/2203.01537/) (2022)
- [AHEAD: Adaptive Hierarchical Decomposition for Range Query under Local Differential Privacy](https://mlsanity.com/paper/2110.07505/) (2021)
- [Classification under local differential privacy](https://mlsanity.com/paper/1912.04629/) (2019)
- [A Neural Database for Differentially Private Spatial Range Queries](https://mlsanity.com/paper/2108.01496/) (2021)
- [Differentially Private Kernel Density Estimation](https://mlsanity.com/paper/2409.01688/) (2024)
- [Grid-Based Decompositions for Spatial Data under Local Differential Privacy](https://mlsanity.com/paper/2407.21624/) (2024)

## Abstract (arXiv)

Density-adaptive domain discretization is essential for high-utility privacy-preserving analytics but remains challenging under Local Differential Privacy (LDP) due to the privacy-budget costs associated with iterative refinement. We propose a novel framework, Semi-Local Differential Privacy (SLDP), that assigns a privacy region to each user based on local density and defines adjacency by the potential movement of a point within its privacy region. We present an interactive (varepsilon, δ)-SLDP protocol, orchestrated by an honest-but-curious server over a public channel, to estimate these regions privately. Crucially, our framework decouples the privacy cost from the number of refinement iterations, allowing for high-resolution grids without additional privacy budget cost. We experimentally demonstrate the framework's effectiveness on estimation tasks across synthetic and real-world datasets.