# Privacy-Utility Trades in Crowdsourced Signal Map Obfuscation

arXiv: https://arxiv.org/abs/2201.04782 · HTML: https://mlsanity.com/paper/2201.04782/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 70 references that are in the map.
- First posted on arXiv 13 Jan 2022.

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

- [Advances and Open Problems in Federated Learning](https://mlsanity.com/paper/1912.04977/) (2019)
- [Conditional Generative Adversarial Nets](https://mlsanity.com/paper/1411.1784/) (2014)
- [Federated Learning: Challenges, Methods, and Future Directions](https://mlsanity.com/paper/1908.07873/) (2019)
- [Deep Leakage from Gradients](https://mlsanity.com/paper/1906.08935/) (2019)
- [Beyond Inferring Class Representatives: User-Level Privacy Leakage From Federated Learning](https://mlsanity.com/paper/1812.00535/) (2018)
- [Differentially Private Learning with Adaptive Clipping](https://mlsanity.com/paper/1905.03871/) (2019)
- [Improving the Gaussian Mechanism for Differential Privacy: Analytical Calibration and Optimal Denoising](https://mlsanity.com/paper/1805.06530/) (2018)
- [Threats to Federated Learning: A Survey](https://mlsanity.com/paper/2003.02133/) (2020)
- [A General Approach to Adding Differential Privacy to Iterative Training Procedures](https://mlsanity.com/paper/1812.06210/) (2018)
- [Context-Aware Generative Adversarial Privacy](https://mlsanity.com/paper/1710.09549/) (2017)
- [Generating Multi-Categorical Samples with Generative Adversarial Networks](https://mlsanity.com/paper/1807.01202/) (2018)
- [Optimal Geo-Indistinguishable Mechanisms for Location Privacy](https://mlsanity.com/paper/1402.5029/) (2014)
- [On (The Lack Of) Location Privacy in Crowdsourcing Applications](https://mlsanity.com/paper/1901.04923/) (2019)
- [LoPub: High-Dimensional Crowdsourced Data Publication with Local Differential Privacy](https://mlsanity.com/paper/1612.04350/) (2016)
- [Location Leakage in Federated Signal Maps](https://mlsanity.com/paper/2112.03452/) (2021)

## Built on by (most cited first, 2 of 2)

- [ConcreTizer: Model Inversion Attack via Occupancy Classification and Dispersion Control for 3D Point Cloud Restoration](https://mlsanity.com/paper/2503.06986/) (2025)
- [SpinML: Customized Synthetic Data Generation for Private Training of Specialized ML Models](https://mlsanity.com/paper/2503.03160/) (2025)

## Abstract (arXiv)

Cellular providers and data aggregating companies crowdsource celluar signal strength measurements from user devices to generate signal maps, which can be used to improve network performance. Recognizing that this data collection may be at odds with growing awareness of privacy concerns, we consider obfuscating such data before the data leaves the mobile device. The goal is to increase privacy such that it is difficult to recover sensitive features from the obfuscated data (e.g. user ids and user whereabouts), while still allowing network providers to use the data for improving network services (i.e. create accurate signal maps). To examine this privacy-utility tradeoff, we identify privacy and utility metrics and threat models suited to signal strength measurements. We then obfuscate the measurements using several preeminent techniques, spanning differential privacy, generative adversarial privacy, and information-theoretic privacy techniques, in order to benchmark a variety of promising obfuscation approaches and provide guidance to real-world engineers who are tasked to build signal maps that protect privacy without hurting utility. Our evaluation results, based on multiple, diverse, real-world signal map datasets, demonstrate the feasibility of concurrently achieving adequate privacy and utility, with obfuscation strategies which use the structure and intended use of datasets in their design, and target average-case, rather than worst-case, guarantees.