Overview of Emerging Privacy Technologies

🤖 Synthetic Data with Privacy Built In? Google Just Raised the Bar In the rapidly evolving world of AI, a quiet revolution is underway—not in model size or speed, but in how we train systems responsibly. Google DeepMind just unveiled a powerful proof of concept. At the heart of the work is a deceptively simple question with big implications: Can we generate useful synthetic data using LLMs—without compromising user privacy? 💡 Here’s what makes this different: • Differential Privacy (DP) isn’t added after the fact. It’s integrated during inference—meaning the model never memorizes or leaks sensitive training data. • The research demonstrates that useful, high-quality synthetic datasets (including summaries, FAQs, and customer support dialogues) can be created with mathematically bounded privacy risks. • This isn’t just about compliance. It’s about trust by design—a cornerstone for responsible AI. 🧠 Why this matters: The next frontier in AI isn’t just bigger models. It’s better boundaries. For legal, privacy, and product leaders, this signals a future where: • We can share model-generated content without exposing source data. • We can train on proprietary or sensitive data—ethically and at scale. • And we can measure privacy rigorously—not just promise it. 📍As organizations seek to unlock the value of internal data for LLMs, synthetic data generation with privacy guarantees is becoming more than a research curiosity. It’s a strategic enabler. The takeaway? We’re moving from “how do we anonymize data later?” to “how do we build privacy into the generation process itself?” Now that’s privacy-forward AI. Read the full post here: 👉 https://lnkd.in/gj4fKg7g Comment, connect and follow for more commentary on product counseling and emerging technologies. 👇

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".

𝗧𝗵𝗲 𝗔𝗴𝗲 𝗼𝗳 𝗣𝗿𝗶𝘃𝗮𝗰𝘆 𝗘𝗻𝗵𝗮𝗻𝗰𝗶𝗻𝗴 𝗧𝗲𝗰𝗵𝗻𝗼𝗹𝗼𝗴𝗶𝗲𝘀 (𝗣𝗘𝗧𝘀) 𝗶𝘀 𝗨𝗽𝗼𝗻 𝗨𝘀 As the digital landscape continues to evolve, the spotlight has turned towards a critical ally in our quest for digital trust and privacy: Privacy Enhancing Technologies (PETs). These technologies are not just add-ons or luxuries anymore; they are becoming indispensable tools in ensuring personal and organizational data privacy. At the heart of PETs is the principle that protecting user privacy should not be an afterthought but a foundational aspect of product development. From federated learning, which allows for AI model training without exposing raw data, to differential privacy, which adds random noise to datasets to prevent identification of individuals, PETs offer a new paradigm in how we approach data. The implications of adopting PETs extend beyond compliance and risk mitigation. They are about building trust with consumers and stakeholders, fostering innovation within regulatory boundaries, and leading the way in ethical technology use. As Data Privacy Engineers, it's our responsibility to champion these technologies. We need to ensure that as our world becomes increasingly digitized, it does not come at the expense of our privacy. Let's lead the charge in making PETs a standard in our industry. The future of digital trust depends on it. #pets #privacyengineering #digitaltrust