Privacy-Preserving Strategies for Cross-Industry Collaboration

Every time we share data, we walk a tightrope between utility and privacy. I have seen how the desire to extract value from data can easily collide with the need to protect it. Yet this is not a zero-sum game. Advances in cryptography and privacy-enhancing technologies are making it possible to reconcile these two goals in ways that were unthinkable just a few years ago. My infographic highlights six privacy-preserving techniques that are helping to reshape how we think about secure data sharing. From fully homomorphic encryption, which allows computations on encrypted data, to differential privacy, which injects noise into datasets to hide individual traces, each method reflects a different strategy to maintain control without losing analytical power. Others, like federated analysis and secure multiparty computation, show how collaboration can thrive even when data is never centralized or fully revealed. The underlying message is simple: privacy does not have to be an obstacle to innovation. On the contrary, it can be a design principle that unlocks new forms of responsible collaboration. #Privacy #DataSharing #Cybersecurity #Encryption #DigitalTrust #DataProtection

Your AI models are learning from your most sensitive data. Here's why that should worry you. Most companies don’t stop to ask: what happens to that data once it’s inside the model? 🤯 That’s where Privacy-Preserving Machine Learning (PPML) comes in. It lets you train powerful AI models without ever exposing your raw data. Here's how it works: ⭐ Differential Privacy - Adds mathematical noise to your data so individual records can't be identified, but the AI still learns useful patterns.  E.g. Apple uses this to collect iOS usage stats without exposing individuals. ⭐ Federated Learning - Trains models across multiple devices or organizations without centralizing the data anywhere. E.g Google trains Gboard’s next-word predictions across millions of devices without centralizing keystrokes. ⭐ Homomorphic Encryption - Lets AI process encrypted data without ever decrypting it. E.g. Imagine a bank detecting fraud on encrypted transactions without decrypting them. ⭐ Secure Multi-party Computation - Multiple parties can jointly train a model without sharing their raw data with each other. E.g. Healthcare orgs collaborate on drug discovery without ever exchanging patient records. In a world where everyone is trying to build AI apps and AI native workflows, the companies that figure out PPML first will have a massive competitive advantage and will be able to: ✅ Tap into more data sources ✅ Collaborate across industries ✅ Earn customer trust 👉 What’s your biggest privacy concern with how AI is being used today?

How can the F&B industry use AI to collaborate, innovate, and still protect data privacy? Imagine restaurants teaming up to create an AI-powered app that suggests personalized meals. They want to collaborate and improve the app while keeping customer data and secret recipes safe. Here’s how they can do it using 𝐩𝐫𝐢𝐯𝐚𝐜𝐲-𝐩𝐫𝐞𝐬𝐞𝐫𝐯𝐢𝐧𝐠 techniques: 𝐃𝐢𝐟𝐟𝐞𝐫𝐞𝐧𝐭𝐢𝐚𝐥 𝐏𝐫𝐢𝐯𝐚𝐜𝐲 is like adding a little spice to every order. The model can detect trends—like knowing burgers are popular—but it won't reveal who ordered extra bacon. Customer details stay safe, and only overall patterns are seen. 𝐅𝐞𝐝𝐞𝐫𝐚𝐭𝐞𝐝 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 is like each restaurant improving the model by sharing tips without revealing their secret recipes. The model gets smarter with input from every restaurant, but none of the actual data (like customer preferences) leaves the premises. It’s like chefs sharing cooking tips without spilling their secret sauce. 𝐇𝐨𝐦𝐨𝐦𝐨𝐫𝐩𝐡𝐢𝐜 𝐄𝐧𝐜𝐫𝐲𝐩𝐭𝐢𝐨𝐧 is like having the chef prepare a dish without seeing the ingredients. The data is encrypted (locked), so even though the model processes it to make meal suggestions, it never sees the raw data. Think of it as cooking while the recipe book stays sealed. 𝐒𝐞𝐜𝐮𝐫𝐞 𝐌𝐮𝐥𝐭𝐢-𝐏𝐚𝐫𝐭𝐲 𝐂𝐨𝐦𝐩𝐮𝐭𝐚𝐭𝐢𝐨𝐧 (SMPC) is like chefs tossing ingredients into a shared pot, but no one knows what the others added. The model combines all the inputs to give personalized suggestions, but each restaurant’s data stays hidden, even from each other. 𝐓𝐫𝐮𝐬𝐭𝐞𝐝 𝐄𝐱𝐞𝐜𝐮𝐭𝐢𝐨𝐧 𝐄𝐧𝐯𝐢𝐫𝐨𝐧𝐦𝐞𝐧𝐭𝐬 (TEEs) are like having each chef cook in a super-secure, locked kitchen where no one can tamper with the recipes. Everything happens in a protected environment, ensuring the data stays private and secure from start to finish. Was that easy to digest? Which other industries could benefit from these techniques? #dataprivacy #foodtech #restaurantindustry #twominutedigest

A few months ago, a European client said something that stuck with me: “We don’t have a data problem. We have a border problem.” They weren’t wrong. When data can’t cross national lines, innovation slows down. The challenge isn’t just compliance, it’s collaboration. Here’s the paradox: Every global AI model wants more data. Every regulation wants less movement of it. So how do we build global intelligence when information can’t leave its home country? That’s where privacy-preserving analytics comes in. It’s a way to bring computation to the data instead of exporting the data to computation. → Federated learning trains models locally, sending only insights, not raw data. → Differential privacy ensures individual records remain invisible. → Secure enclaves let companies run analytics behind locked doors, literally. The outcome? Data stays compliant. Teams stay collaborative. And innovation doesn’t stop at the border. The next decade won’t be about breaking data silos. It’ll be about connecting them, safely, lawfully, and intelligently. MATH (AI & ML Tech Hub at T-Hub) T-Hub

Bosch has just published a very interesting assessment regarding cutting-edge privacy-preserving technology. A group of their researchers (such as Sven Trieflinger and Hossein Yalame) have worked with independent lawyers to determine if and when Secure Multiparty Computation (MPC) can be used to process private data cross-border according to the GDPR. Their conclusions make sense, also from my technical perspective: the data stored inside the MPC is not considered private, unless someone has the (legal) means to reconstruct it. You can't use MPC if everyone participating is controlled by the same legal entity (e.g. same company in one country). Once you go cross-border, however, things are different as subsidiaries must follow local privacy laws and thus have an obligation not to break privacy. However, they additionally recommend to put contractual agreements in place regarding the honest participation in MPC. This is particularly interesting when outsourcing the computation (or management thereof) to third-party technology providers. The full report can be found here: https://lnkd.in/dEwdtecZ