Every organization wants to capture the best possible insight from the data they collect and this is especially true for the contextual and personal data. But this is a dangerous practice, because personal information can always be abused. To overcome these issues, researchers developed a privacy-preserving technology called differential privacy.
What is Differential Privacy?
Differential privacy is a mathematical definition that provides a guarantee of safety for any one individual by enabling statistical information to be released about a group of individuals while ensuring the privacy of anyone individual in the group. This is done by perturbing the data with noise that (generally) hides the individual points and persons from the data.
What is Differential Privacy and How Does it Work?
Differential privacy introduces a small random noise to the result of a statistical query. This noise added is tuned well to maintain the best privacy-utility trade-off. The privacy of individuals is protected because, with noise added, it is hard to tell whether the specific individual was a part of the query or not.
Use Cases of Differential Privacy
Differential privacy can be used in a number of different ways:
Census Data: Aggregate population statistics where individual privacy is protected.
Medical research: Examining medical records to find trends and patterns, all without disclosure of patient identity.
Machine Learning: Train machine learning models on sensitive data, while still being exposed to do not having access to individual data.
Location Tracking: Studying location data for movement patterns without compromising user privacy.
Issues and Future Paths
Differential privacy is a potent weapon, but it has some challenges:
Feature #02: Privacy vs. Utility As we would like the released information to be as useful as possible, it is important to make a trade-off between the privacy and the utility of the released information.
Given that: — Based on the t.smtp and e. smtp — Differential privacy doesn’t come for free. It can prove to be computationally expensive to implement particularly on larger datasets.
Complicating Matters: Since this concern applies to data commissioned across a wide swathe of stakeholders, it is not only more complicated development-wise, but also more difficult with regards to the algorithms and methods we would need to use to analyze differentially private data.
