Local Differential Privacy: a tutorial

Abstract

In the past decade analysis of big data has proven to be extremely valuable in many contexts. Local Differential Privacy (LDP) is a state-of-the-art approach which allows statistical computations while protecting each individual user's privacy. Unlike Differential Privacy no trust in a central authority is necessary as noise is added to user inputs locally. In this paper we give an overview over different LDP algorithms for problems such as locally private heavy hitter identification and spatial data collection. Finally, we will give an outlook on open problems in LDP.

References in corpus (3)

• Privacy Loss in Apple's Implementation of Differential Privacy on MacOS 10.12
• Collecting and Analyzing Data from Smart Device Users with Local Differential Privacy
• Practical Locally Private Heavy Hitters

Cited by in corpus (7)

• A Comprehensive Survey on Local Differential Privacy Toward Data Statistics and Analysis
• Local Differential Privacy and Its Applications: A Comprehensive Survey
• Fair Sequential Selection Using Supervised Learning Models
• On Lightweight Privacy-Preserving Collaborative Learning for Internet of Things by Independent Random Projections
• When Differential Privacy Meets Interpretability: A Case Study
• No-Regret Algorithms for Private Gaussian Process Bandit Optimization
• Mobility Data Analysis and Applications: A mid-year 2021 Survey