# Collecting and Analyzing Data from Smart Device Users with Local Differential Privacy

arXiv: https://arxiv.org/abs/1606.05053 · HTML: https://mlsanity.com/paper/1606.05053/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Jun 2016.

## Built on by (most cited first, 25 of 25)

- [Stronger Privacy for Federated Collaborative Filtering with Implicit Feedback](https://mlsanity.com/paper/2105.03941/) (2021)
- [Optimal Compression of Locally Differentially Private Mechanisms](https://mlsanity.com/paper/2111.00092/) (2021)
- [Privacy Amplification via Compression: Achieving the Optimal Privacy-Accuracy-Communication Trade-off in Distributed Mean Estimation](https://mlsanity.com/paper/2304.01541/) (2023)
- [Fairness, Integrity, and Privacy in a Scalable Blockchain-based Federated Learning System](https://mlsanity.com/paper/2111.06290/) (2021)
- [OpBoost: A Vertical Federated Tree Boosting Framework Based on Order-Preserving Desensitization](https://mlsanity.com/paper/2210.01318/) (2022)
- [A Huber Loss Minimization Approach to Mean Estimation under User-level Differential Privacy](https://mlsanity.com/paper/2405.13453/) (2024)
- [Residue-based Label Protection Mechanisms in Vertical Logistic Regression](https://mlsanity.com/paper/2205.04166/) (2022)
- [RFLPA: A Robust Federated Learning Framework against Poisoning Attacks with Secure Aggregation](https://mlsanity.com/paper/2405.15182/) (2024)
- [Federated Analytics: A survey](https://mlsanity.com/paper/2302.01326/) (2023)
- [Exact Optimality of Communication-Privacy-Utility Tradeoffs in Distributed Mean Estimation](https://mlsanity.com/paper/2306.04924/) (2023)
- [Frequency Estimation Under Multiparty Differential Privacy: One-shot and Streaming](https://mlsanity.com/paper/2104.01808/) (2021)
- [Statistical Theory of Differentially Private Marginal-based Data Synthesis Algorithms](https://mlsanity.com/paper/2301.08844/) (2023)
- [Private Vector Mean Estimation in the Shuffle Model: Optimal Rates Require Many Messages](https://mlsanity.com/paper/2404.10201/) (2024)
- ["You Can't Fix What You Can't Measure": Privately Measuring Demographic Performance Disparities in Federated Learning](https://mlsanity.com/paper/2206.12183/) (2022)
- [Breaking the Communication-Privacy-Accuracy Tradeoff with f-Differential Privacy](https://mlsanity.com/paper/2302.09624/) (2023)
- [Locally Differentially Private Bayesian Inference](https://mlsanity.com/paper/2110.14426/) (2021)
- [Federated Privacy-preserving Collaborative Filtering for On-Device Next App Prediction](https://mlsanity.com/paper/2303.04744/) (2023)
- [Beyond Statistical Estimation: Differentially Private Individual Computation via Shuffling](https://mlsanity.com/paper/2406.18145/) (2024)
- [PREAMBLE: Private and Efficient Aggregation via Block Sparse Vectors](https://mlsanity.com/paper/2503.11897/) (2025)
- [Linear Model Against Malicious Adversaries with Local Differential Privacy](https://mlsanity.com/paper/2202.02448/) (2022)
- [Production of Categorical Data Verifying Differential Privacy: Conception and Applications to Machine Learning](https://mlsanity.com/paper/2204.00850/) (2022)
- [Federated Hypergraph Learning with Local Differential Privacy: Toward Privacy-Aware Hypergraph Structure Completion](https://mlsanity.com/paper/2408.05160/) (2024)
- [Classification Under Local Differential Privacy with Model Reversal and Model Averaging](https://mlsanity.com/paper/2602.05797/) (2026)
- [Robust Estimation of Sparse Numerical Vectors under Local Differential Privacy](https://mlsanity.com/paper/2607.27815/) (2026)
- [ABC: Numerical Data Collection under Local Differential Privacy without Prior Knowledge](https://mlsanity.com/paper/2608.05737/) (2026)

## Abstract (arXiv)

Organizations with a large user base, such as Samsung and Google, can potentially benefit from collecting and mining users' data. However, doing so raises privacy concerns, and risks accidental privacy breaches with serious consequences. Local differential privacy (LDP) techniques address this problem by only collecting randomized answers from each user, with guarantees of plausible deniability; meanwhile, the aggregator can still build accurate models and predictors by analyzing large amounts of such randomized data. So far, existing LDP solutions either have severely restricted functionality, or focus mainly on theoretical aspects such as asymptotical bounds rather than practical usability and performance. Motivated by this, we propose Harmony, a practical, accurate and efficient system for collecting and analyzing data from smart device users, while satisfying LDP. Harmony applies to multi-dimensional data containing both numerical and categorical attributes, and supports both basic statistics (e.g., mean and frequency estimates), and complex machine learning tasks (e.g., linear regression, logistic regression and SVM classification). Experiments using real data confirm Harmony's effectiveness.