Privacy Enhancing Technologies (PETs)

Privacy-Preserving AI: Power to Innovate Without Compromising Privacy

Motivation

Being a practical professional, I used to brush off the whole data privacy thing. I mean, why worry about it when I was laser-focused on crunching the numbers, delivering a killer model, and bringing it to production so it could ‘do magic’, right?

But, one day my team hit a brick wall—we weren’t allowed to build several awesome models because, guess what? ‘The privacy isn’t in order.’ And trust me, having your cool AI projects killed because of privacy issues? That’s really not cool. Super frustrating.

That’s when it hit me—If you can’t beat them, join them. Take control of privacy, and watch your projects fly . Trust me, it’ll save you a whole lot of headaches later on!

If you’re part of a data science, data engineering, analytics, or AI team, you’ve probably faced this challenge head-on. Fear not! There are some brilliant ways to build privacy-preserving AI without breaking the bank—or the trust of your customers. Let’s dive into the coolest approaches that let you innovate, comply with regulations like GDPR, and still deliver killer AI solutions.

Privacy-Enhancing Technologies (PETs) at a glance

As organizations process more personal and sensitive data, Privacy-Enhancing Technologies (PETs) play a crucial role in ensuring compliance, building trust, and protecting individuals — especially in the context of AI and analytics.

Here’s a condensed overview of key PET methods and how they work:

PET Method

What It Does

Use Case Example

Anonymization

Removes identifiable elements from data so individuals can’t be re-identified.

Public health dashboards, open datasets

Pseudonymization

Replaces identifiers with tokens, but allows re-identification under control.

HR records, customer profiles

Differential Privacy (DP)

Adds noise to outputs or models to prevent leaking individual data.

Training AI models on user data

Federated Learning (FL)

Trains AI models across devices without sharing raw data.

Mobile apps, distributed healthcare research

Homomorphic Encryption (HE)

Allows data to be computed on while still encrypted.

Secure cloud computation, finance

Secure Multi-Party Computation (SMPC)

Enables multiple parties to compute jointly without revealing their inputs.

Collaborative analytics across organizations

Synthetic Data

Generates artificial datasets with similar structure to real data.

Testing, training models without exposing real data

Data Minimization

Collects and uses only the data strictly needed for a given task.

Any privacy-aware system design

What methods are the best?

Well, that quite a list. As to me, there are some really interesting techniques, which I would like to explore further. In my next articles I will review each method, one by one to, together with the use cases and example of codes.