How Decentralized Systems Improve Data Privacy

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

Explore how federated learning enables collaborative model training across decentralized devices, ensuring data privacy and security in sectors like healthcare and finance. 𝗜𝗻 𝘁𝗵𝗶𝘀 𝗮𝗿𝘁𝗶𝗰𝗹𝗲, 𝘆𝗼𝘂’𝗹𝗹 𝗹𝗲𝗮𝗿𝗻: ➡️ Why Federated Learning Is the Future of Private AI ➡️ Introduction of Federated Learning and its Types ➡️ Data Protection with Federated AI ➡️ How Federated Learning Works (Step-by-Step Guide) ➡️ Real Word Applications and Benefits of Federated Learning ➡️ Challenges and Limitations of Federated Learning ➡️ The Future Trend and Experts Insight of Federated Learning ➡️ Market Trends and Growth of Federated Learning 𝗙𝗔𝗤𝘀: 𝟭. 𝗪𝗵𝗮𝘁 𝗶𝘀 𝘁𝗵𝗲 𝗺𝗮𝗶𝗻 𝗮𝗱𝘃𝗮𝗻𝘁𝗮𝗴𝗲 𝗼𝗳 𝗳𝗲𝗱𝗲𝗿𝗮𝘁𝗲𝗱 𝗹𝗲𝗮𝗿𝗻𝗶𝗻𝗴? Federated Learning enhances data privacy by training models locally on devices, ensuring that raw data never leaves the device. 𝟮. 𝗛𝗼𝘄 𝗱𝗼𝗲𝘀 𝗳𝗲𝗱𝗲𝗿𝗮𝘁𝗲𝗱 𝗹𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗱𝗶𝗳𝗳𝗲𝗿 𝗳𝗿𝗼𝗺 𝘁𝗿𝗮𝗱𝗶𝘁𝗶𝗼𝗻𝗮𝗹 𝗔𝗜? Unlike traditional AI, which requires centralized data collection, FL trains models across decentralized devices, sharing only model updates. 𝟯. 𝗜𝘀 𝗳𝗲𝗱𝗲𝗿𝗮𝘁𝗲𝗱 𝗹𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝘀𝗲𝗰𝘂𝗿𝗲? While FL offers improved privacy, it still faces security challenges like model poisoning and requires robust security measures. 𝟰. 𝗪𝗵𝗶𝗰𝗵 𝗶𝗻𝗱𝘂𝘀𝘁𝗿𝗶𝗲𝘀 𝗯𝗲𝗻𝗲𝗳𝗶𝘁 𝗺𝗼𝘀𝘁 𝗳𝗿𝗼𝗺 𝗳𝗲𝗱𝗲𝗿𝗮𝘁𝗲𝗱 𝗹𝗲𝗮𝗿𝗻𝗶𝗻𝗴? Industries handling sensitive data, such as healthcare, finance, and mobile technology, benefit significantly from FL. 𝟱. 𝗪𝗵𝗮𝘁 𝘁𝗼𝗼𝗹𝘀 𝘀𝘂𝗽𝗽𝗼𝗿𝘁 𝗳𝗲𝗱𝗲𝗿𝗮𝘁𝗲𝗱 𝗹𝗲𝗮𝗿𝗻𝗶𝗻𝗴? Frameworks like TensorFlow Federated, PySyft, and Flower are popular tools for implementing FL. 📌 𝗣𝗦: Curious how Federated Learning can boost data privacy without sacrificing performance? Let’s connect https://lnkd.in/d7FR8yK2 and explore https://lnkd.in/dSrCAq45 privacy-first AI strategies for your organization or research. #FederatedLearning #PrivacyPreservingAI #EdgeComputing #MachineLearning #AIethics

From Trusted to Trustless Execution Environments. Listen to GenZ's slang If you are unfamiliar with the words: ‘sus’ (suspicious), ‘cap’ (lie), ‘glazed’ (exaggerated) or ‘based’ (based in fact), don’t worry - you’re just like me, old. But more interestingly, GenZ's slang tells us a lot about their perceived world, a world which basically cannot be trusted. And as companies ‘update’ their terms of services to make AI training easier, our legal privacy protections are hollowed, making us even more vulnerable to unfair and deceptive practices. https://shorturl.at/SlCHu So in this post I would like to review a few privacy enhancing technologies and suggest (of course) that decentralizing these solutions is key to regain trust. 1- First, differential privacy (DP) that ensures algorithms maintain dataset privacy while in training. Datasets are subdivided, limiting the impact of a data breach. Though fast, access to private data is still needed and there is a privacy-accuracy trade off during the dataset splitting. 2- Zero knowledge proof (ZKP) is a method where one proves to another that the data output is true without sharing raw data. This allows data owners to ‘trust’ AI, though the proofs are compute-intense. 3- Federated Learning allows multiple clients to train a model without the data leaving their dataset. This computation is local, distributed and private. 4- Fully homomorphic encryption (FHE) as its name suggests, can compute encrypted data. It is effective and private, as well as quantum-resistant. 5- Secure multiparty computation (MPC) allows parties to jointly analyze data and privately train ML. 6 - Trusted Execution Environments (TEE) are hardware solutions usually installed in the memory (enclave) and protects computers from malicious software and unauthorized access. TEE offers the most robust private training, and is especially useful when data owners are reluctant to share data. (below) Finally, and the point of this post is that privacy enhancing technologies are not stand alone computational advances. They represent a path to restoring Trust into this world. Privacy is not just about verifiable proofs and hardware-assisted solutions that 'plant trust' in our CPU’s and GPU’s. https://shorturl.at/GIfvG It’s about insisting that our foundation model AI training should be decentralized, private and individual (zk-ILM’s), using an epistemic (language) base of empathy, Love and humanity. In order to build a better internet, we first need to be better builders and better versions of ourselves, and I seriously mean that. "No cap, no sus, not glaze, and totally based".