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.
What can we do about it?
I have done some research of my own. These are the current Privacy-Preserving AI Methods for the Data Science, Data Engineering, Data Analytics, Artificial Intelligence (AI):
Method
Description
Examples
1) Randomization
Removing the strong link between the data and the individual
Noise addition (Differential Privacy), permutation / shuffling (Randomized Response), swapping, removal
2) Generalization
Generalizing attributes of data subjects by modifying the respective scale or order of magnitude
Aggregation, k -anonymity, l-diversity, t-closeness
3) Encryption
Enables computation on encrypted data without revealing it
Secure data analytics on encrypted health records, Homomorphic Encryption
4) Federated Learning
Allows model training on decentralized data without sharing raw data
Collaborative AI in healthcare, finance
5) Synthetic Data
Generates artificial data for analysis, preserving statistical integrity
Training models on fake data that resembles real data
6) Secure Multi-Party Computation
Enables joint computation on private data without sharing it.
Multi-institutional studies in finance, healthcare
5) Other methods
Working to some extend
Anonymization, Pseudonymization, Data Masking, RBAC, Consent Management..
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. Â
Did I miss any other method? Let me know! Â
