Differential Privacy: Safeguarding Data Without Sacrificing Innovation
This article has practical angle of the subject of Differential Privacy (DP). There are lots of research and scientific publications already, view list below. The aim is to illustrate that the method of DP has good potential to be applied 
in the real use cases on the day-to-day base, especially if your tech team takes privacy and ethics requirements seriously.
This article is the second in a series on Privacy Enhancing Technologies (PETs) by Yul.ai. Click here to check out the other articles in this series.
What is Differential Privacy (DP):
Differential privacy is one of the promising methods for ensuring privacy during data analysis and AI model training. The result of implementing of such an algorithm would be that by looking at the output, one cannot tell whether any individual’s data was included in the original dataset or not. This is exactly what data privacy aims!
How DP Works:
Noise is added to the data in such a way that it can prevent the re-identification of sensitive information and protect individuals’ privacy. The level of privacy is controlled by a parameter called epsilon(ε): the privacy loss incurred by researchers in the dataset. Larger values indicate less privacy and more accuracy.
Use of such an algorithm ensures, that individual-level information about participants in the database is not leaked ⚡️.
Key Applications:
Machine Learning Models: Differential privacy can be integrated into AI training pipelines to prevent leakage of personal data. Companies like Google and Apple have implemented differential privacy in their data collection processes to gather insights without exposing individual users.- Data Analytics: Used in statistical queries and aggregate data analysis where privacy concerns arise, such as demographic analysis, census data reporting, etc…
More theory and materials on Differential Privacy:
- 📝Harvard University did extensive study link of this method.
- 📝DF for Programming: link;
- 📝Cornell University: link to search on DF, lots of different scenarios and solutions;
- 📝UC Berkley School of Information: link;
Use Cases in Action:
Well, lots of theory, and this looks lovely, but how about practical application? Let’s take a look at how some of the big and small players are using Differential Privacy to their advantage.
1. Google’s Mobility Reports
When Google started releasing COVID-19 mobility reports, they used Differential Privacy to track movement trends while safeguarding user privacy. These reports helped governments and health organizations understand how people’s movement patterns changed during lockdowns without compromising individuals’ locations or identities. By adding noise to their aggregate data, Google ensured that the reports were useful but did not expose sensitive personal information.
- Use Case Source: Google’s Differential Privacy Usage
- 🎼 Code/Repository: explore Google’s Open-Source Differential Privacy Library here.
2. Apple’s iOS Analytics
Apple uses Differential Privacy across iOS and macOS to improve features like QuickType (the keyboard suggestion tool), emoji suggestions, and more—without ever knowing what individuals are typing. They add noise to user data before sending it back to Apple’s servers, ensuring privacy while enabling system-wide improvements.
- 🎼 Use Case Source: Apple’s Differential Privacy
3. U.S. Census Bureau
When the U.S. Census Bureau released data from the 2020 census, they implemented Differential Privacy to protect individual respondents’ data. Given the sensitivity of census data, ensuring privacy was a top priority. By adding noise to the data, they ensured that individuals couldn’t be re-identified, while still providing valuable insights for government planning and research.
- 🎼 Use Case Source: US Census Bureau’s Differential Privacy Application;
- Tools and Techniques: For this application, they used their own techniques, but you can apply similar concepts with open-source libraries.
4. Study: Private targeting with Differential Privacy at EUR (the Netherlands)
This study reviews two private targeting strategies that mathematically guarantee privacy protection through differential privacy. Application of the proposed strategies in two increasingly complex simulation studies and in a field experiment with over 400,000 customers.
- 🎼 Code/Repository: PrivateTargetingStrategies github
5. Healthcare Cost and Utilisation, US:
Usage of differential privacy technique to study healthcare utilization and costs across the US while protecting the privacy of individual patients.
- 🎼 Project information: link
I have also found some more evidence of other big players using DP, like Microsoft, Likedin, Meta. It is not my intention to provide a full list of all deployments with DP. I am merely looking for the practical application of DP, the next steps for this research.
How you can use Differential Privacy yourself?
Okay, we’ve seen the use cases do exist, but what options do data practitioners have, now? These options would be a good starting point:
1. TensorFlow Privacy
If you’re already building AI models with TensorFlow, that would be even easier for you. TensorFlow Privacy integrates Differential Privacy into machine learning workflows. You can control the privacy budget (ε) and train models while ensuring data privacy.
- ⏯️ Repository: TensorFlow Privacy GitHub
2. PySyft + PyTorch
Prefer PyTorch? No worries. PySyft by OpenMined allows you to train AI models with Differential Privacy. It offers a suite of privacy-enhancing techniques, including federated learning and encrypted computation, alongside Differential Privacy.
- ⏯️ Repository: PySyft GitHub
3. Google’s Open-Source Library
If you want to get hands-on with Google, check out Google’s Open-Source Differential Privacy Library. It’s designed to make implementing Differential Privacy in any system as straightforward as possible.
Repository: Google Differential Privacy GitHub
4. Other libraries and frameworks:
- ⏯️ PipelineDP by Google;
- ⏯️ Tumult Analytics by Tumult Labs;
- ⏯️ OpenDP by the privacy team at Harvard;
- ⏯️ Diffprivlib by IBM
Let’s Wrap It Up
Differential Privacy looks like a game changer in the world of Privacy-Preserving AI. It allows data practitioners to extract valuable insights from data without compromising privacy.
If you’re building something what is going into production, it would make sense to make privacy as a feature, not an afterthought!
Happy building 🚀!
I am looking for the tech teams who would like to implement Differential Privacy in real use- cases. PM me. 💬 Questions, suggestions, considerations are welcome🔥!
