# Collecting Telemetry Data Privately

arXiv: https://arxiv.org/abs/1712.01524 · HTML: https://mlsanity.com/paper/1712.01524/

## Key facts

- Cited by 149 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Dec 2017.

## Built on by (most cited first, 30 of 149)

- [Deep Learning with Label Differential Privacy](https://mlsanity.com/paper/2102.06062/) (2021)
- [Foundation Models and Fair Use](https://mlsanity.com/paper/2303.15715/) (2023)
- [Learning with User-Level Privacy](https://mlsanity.com/paper/2102.11845/) (2021)
- [Privacy of Noisy Stochastic Gradient Descent: More Iterations without More Privacy Loss](https://mlsanity.com/paper/2205.13710/) (2022)
- [Connect the Dots: Tighter Discrete Approximations of Privacy Loss Distributions](https://mlsanity.com/paper/2207.04380/) (2022)
- [Private Convex Optimization via Exponential Mechanism](https://mlsanity.com/paper/2203.00263/) (2022)
- [High Dimensional Differentially Private Stochastic Optimization with Heavy-tailed Data](https://mlsanity.com/paper/2107.11136/) (2021)
- [Federated Learning with Local Differential Privacy: Trade-offs between Privacy, Utility, and Communication](https://mlsanity.com/paper/2102.04737/) (2021)
- [Private Robust Estimation by Stabilizing Convex Relaxations](https://mlsanity.com/paper/2112.03548/) (2021)
- [Generalized Linear Bandits with Local Differential Privacy](https://mlsanity.com/paper/2106.03365/) (2021)
- [Stronger Privacy Amplification by Shuffling for R'enyi and Approximate Differential Privacy](https://mlsanity.com/paper/2208.04591/) (2022)
- [Lossless Compression of Efficient Private Local Randomizers](https://mlsanity.com/paper/2102.12099/) (2021)
- [Differentially private inference via noisy optimization](https://mlsanity.com/paper/2103.11003/) (2021)
- [A Critical Review on the Use (and Misuse) of Differential Privacy in Machine Learning](https://mlsanity.com/paper/2206.04621/) (2022)
- [Differentially Private Label Protection in Split Learning](https://mlsanity.com/paper/2203.02073/) (2022)
- [Optimal and Differentially Private Data Acquisition: Central and Local Mechanisms](https://mlsanity.com/paper/2201.03968/) (2022)
- [The Internet of Federated Things (IoFT): A Vision for the Future and In-depth Survey of Data-driven Approaches for Federated Learning](https://mlsanity.com/paper/2111.05326/) (2021)
- [Differentially Private SGDA for Minimax Problems](https://mlsanity.com/paper/2201.09046/) (2022)
- [Learning to Generate Image Embeddings with User-level Differential Privacy](https://mlsanity.com/paper/2211.10844/) (2022)
- [Differentially Private SGD with Non-Smooth Losses](https://mlsanity.com/paper/2101.08925/) (2021)
- [Locally Private k-Means in One Round](https://mlsanity.com/paper/2104.09734/) (2021)
- [Dopamine: Differentially Private Federated Learning on Medical Data](https://mlsanity.com/paper/2101.11693/) (2021)
- [Private Prediction Sets](https://mlsanity.com/paper/2102.06202/) (2021)
- [Wide Network Learning with Differential Privacy](https://mlsanity.com/paper/2103.01294/) (2021)
- [The Role of Cross-Silo Federated Learning in Facilitating Data Sharing in the Agri-Food Sector](https://mlsanity.com/paper/2104.07468/) (2021)
- [On the Renyi Differential Privacy of the Shuffle Model](https://mlsanity.com/paper/2105.05180/) (2021)
- [Towards Private Learning on Decentralized Graphs with Local Differential Privacy](https://mlsanity.com/paper/2201.09398/) (2022)
- [Faster Privacy Accounting via Evolving Discretization](https://mlsanity.com/paper/2207.04381/) (2022)
- [Privately Learning Mixtures of Axis-Aligned Gaussians](https://mlsanity.com/paper/2106.02162/) (2021)
- [Private Convex Optimization in General Norms](https://mlsanity.com/paper/2207.08347/) (2022)

## Abstract (arXiv)

The collection and analysis of telemetry data from users' devices is routinely performed by many software companies. Telemetry collection leads to improved user experience but poses significant risks to users' privacy. Locally differentially private (LDP) algorithms have recently emerged as the main tool that allows data collectors to estimate various population statistics, while preserving privacy. The guarantees provided by such algorithms are typically very strong for a single round of telemetry collection, but degrade rapidly when telemetry is collected regularly. In particular, existing LDP algorithms are not suitable for repeated collection of counter data such as daily app usage statistics. In this paper, we develop new LDP mechanisms geared towards repeated collection of counter data, with formal privacy guarantees even after being executed for an arbitrarily long period of time. For two basic analytical tasks, mean estimation and histogram estimation, our LDP mechanisms for repeated data collection provide estimates with comparable or even the same accuracy as existing single-round LDP collection mechanisms. We conduct empirical evaluation on real-world counter datasets to verify our theoretical results. Our mechanisms have been deployed by Microsoft to collect telemetry across millions of devices.