What are the 3 Variants of Differential Privacy?
Local vs Global vs Distributed Differential Privacy
Differential privacy is a statistical technique used to ensure the privacy and confidentiality of individuals’ data when being used in broader analyses or shared publicly. It achieves this by adding controlled random noise to the data to mask the contribution of individual data points, making it difficult to derive anything specific about the individual from the output.
Differential privacy can be categorized into 3 main variants to cater different data management scenarios:
- Local differential privacy — Applied at the level of individual devices or sensors.
- Global differential privacy — Applied at the central server level.
- Distributed differential privacy — Applied when the data is distributed across multiple servers or devices.
Each of these variant is designed to balance privacy protection with the data usage, allowing organizations to maintain privacy standards while using the data for meaningful insights.
1. Local Differential Privacy (LDP)
In the local differential privacy approach, the data owner is responsible for randomizing each data point before sending it to a data…
