How Technology Influences Privacy Management

This new white paper by Stanford Institute for Human-Centered Artificial Intelligence (HAI) titled "Rethinking Privacy in the AI Era" addresses the intersection of data privacy and AI development, highlighting the challenges and proposing solutions for mitigating privacy risks. It outlines the current data protection landscape, including the Fair Information Practice Principles, GDPR, and U.S. state privacy laws, and discusses the distinction and regulatory implications between predictive and generative AI. The paper argues that AI's reliance on extensive data collection presents unique privacy risks at both individual and societal levels, noting that existing laws are inadequate for the emerging challenges posed by AI systems, because they don't fully tackle the shortcomings of the Fair Information Practice Principles (FIPs) framework or concentrate adequately on the comprehensive data governance measures necessary for regulating data used in AI development. According to the paper, FIPs are outdated and not well-suited for modern data and AI complexities, because: - They do not address the power imbalance between data collectors and individuals. - FIPs fail to enforce data minimization and purpose limitation effectively. - The framework places too much responsibility on individuals for privacy management. - Allows for data collection by default, putting the onus on individuals to opt out. - Focuses on procedural rather than substantive protections. - Struggles with the concepts of consent and legitimate interest, complicating privacy management. It emphasizes the need for new regulatory approaches that go beyond current privacy legislation to effectively manage the risks associated with AI-driven data acquisition and processing. The paper suggests three key strategies to mitigate the privacy harms of AI: 1.) Denormalize Data Collection by Default: Shift from opt-out to opt-in data collection models to facilitate true data minimization. This approach emphasizes "privacy by default" and the need for technical standards and infrastructure that enable meaningful consent mechanisms. 2.) Focus on the AI Data Supply Chain: Enhance privacy and data protection by ensuring dataset transparency and accountability throughout the entire lifecycle of data. This includes a call for regulatory frameworks that address data privacy comprehensively across the data supply chain. 3.) Flip the Script on Personal Data Management: Encourage the development of new governance mechanisms and technical infrastructures, such as data intermediaries and data permissioning systems, to automate and support the exercise of individual data rights and preferences. This strategy aims to empower individuals by facilitating easier management and control of their personal data in the context of AI. by Dr. Jennifer King Caroline Meinhardt Link: https://lnkd.in/dniktn3V

How do we balance AI personalization with the privacy fundamental of data minimization? Data minimization is a hallmark of privacy, we should collect only what is absolutely necessary and discard it as soon as possible. However, the goal of creating the most powerful, personalized AI experience seems fundamentally at odds with this principle. Why? Because personalization thrives on data. The more an AI knows about your preferences, habits, and even your unique writing style, the more it can tailor its responses and solutions to your specific needs. Imagine an AI assistant that knows not just what tasks you do at work, but how you like your coffee, what music you listen to on the commute, and what content you consume to stay informed. This level of personalization would really please the user. But achieving this means AI systems would need to collect and analyze vast amounts of personal data, potentially compromising user privacy and contradicting the fundamental of data minimization. I have to admit even as a privacy evangelist, I like personalization. I love that my car tries to guess where I am going when I click on navigation and it's 3 choices are usually right. For those playing at home, I live a boring life, it's 3 choices are usually, My son's school, Our Church, or the soccer field where my son plays. So how do we solve this conflict? AI personalization isn't going anywhere, so how do we maintain privacy? Here are some thoughts: 1) Federated Learning: Instead of storing data in centralized servers, federated learning trains AI algorithms locally on your device. This approach allows AI to learn from user data without the data ever leaving your device, thus aligning more closely with data minimization principles. 2) Differential Privacy: By adding statistical noise to user data, differential privacy ensures that individual data points cannot be identified, even while still contributing to the accuracy of AI models. While this might limit some level of personalization, it offers a compromise that enhances user trust. 3) On-Device Processing: AI could be built to process and store personalized data directly on user devices rather than cloud servers. This ensures that data is retained by the user and not a third party. 4) User-Controlled Data Sharing: Implementing systems where users have more granular control over what data they share and when can give people a stronger sense of security without diluting the AI's effectiveness. Imagine toggling data preferences as easily as you would app permissions. But, most importantly, don't forget about Transparency! Clearly communicate with your users and obtain consent when needed. So how do y'all think we can strike this proper balance?

AI is revolutionizing security, but at what cost to our privacy? As AI technologies become more integrated into sectors like healthcare, finance, and law enforcement, they promise enhanced protection against threats. But this progress comes with a serious question: Are we sacrificing our privacy in the name of security? Here’s why this matters: → AI’s Role in Security From facial recognition to predictive policing, AI is transforming security measures. These systems analyze vast amounts of data quickly, identifying potential threats and improving responses. But there’s a catch: they also rely on sensitive personal data to function. → Data Collection & Surveillance Risks AI systems need a lot of data—often including health records, financial details, and biometric data. Without proper safeguards, this can lead to privacy breaches, with potential unauthorized tracking via technologies like facial recognition. → The Black Box Dilemma AI systems often operate in a "black box," meaning users don’t fully understand how their data is used or how decisions are made. This lack of transparency raises serious concerns about accountability and trust. → Bias and Discrimination AI isn’t immune to bias. If systems are trained on flawed data, they may perpetuate inequality, especially in areas like hiring or law enforcement. This can lead to discriminatory practices that violate personal rights. → Finding the Balance The ethical dilemma: How do we balance the benefits of AI-driven security with the need to protect privacy? With AI regulations struggling to keep up, organizations must tread carefully to avoid violating civil liberties. The Takeaway: AI in security offers significant benefits, but we must approach it with caution. Organizations need to prioritize privacy through transparent practices, minimal data collection, and continuous audits. Let’s rethink AI security—making sure it’s as ethical as it is effective. What steps do you think organizations should take to protect privacy? Share your thoughts. 👇

I'm increasingly convinced that we need to treat "AI privacy" as a distinct field within privacy, separate from but closely related to "data privacy". Just as the digital age required the evolution of data protection laws, AI introduces new risks that challenge existing frameworks, forcing us to rethink how personal data is ingested and embedded into AI systems. Key issues include: 🔹 Mass-scale ingestion – AI models are often trained on huge datasets scraped from online sources, including publicly available and proprietary information, without individuals' consent. 🔹 Personal data embedding – Unlike traditional databases, AI models compress, encode, and entrench personal data within their training, blurring the lines between the data and the model. 🔹 Data exfiltration & exposure – AI models can inadvertently retain and expose sensitive personal data through overfitting, prompt injection attacks, or adversarial exploits. 🔹 Superinference – AI uncovers hidden patterns and makes powerful predictions about our preferences, behaviours, emotions, and opinions, often revealing insights that we ourselves may not even be aware of. 🔹 AI impersonation – Deepfake and generative AI technologies enable identity fraud, social engineering attacks, and unauthorized use of biometric data. 🔹 Autonomy & control – AI may be used to make or influence critical decisions in domains such as hiring, lending, and healthcare, raising fundamental concerns about autonomy and contestability. 🔹 Bias & fairness – AI can amplify biases present in training data, leading to discriminatory outcomes in areas such as employment, financial services, and law enforcement. To date, privacy discussions have focused on data - how it's collected, used, and stored. But AI challenges this paradigm. Data is no longer static. It is abstracted, transformed, and embedded into models in ways that challenge conventional privacy protections. If "AI privacy" is about more than just the data, should privacy rights extend beyond inputs and outputs to the models themselves? If a model learns from us, should we have rights over it? #AI #AIPrivacy #Dataprivacy #Dataprotection #AIrights #Digitalrights

Privacy isn’t a policy layer in AI. It’s a design constraint. The new EDPB guidance on LLMs doesn’t just outline risks. It gives builders, buyers, and decision-makers a usable blueprint for engineering privacy - not just documenting it. The key shift? → Yesterday: Protect inputs → Today: Audit the entire pipeline → Tomorrow: Design for privacy observability at runtime The real risk isn’t malicious intent. It’s silent propagation through opaque systems. In most LLM systems, sensitive data leaks not because someone intended harm but because no one mapped the flows, tested outputs, or scoped where memory could resurface prior inputs. This guidance helps close that gap. And here’s how to apply it: For Developers: • Map how personal data enters, transforms, and persists • Identify points of memorization, retention, or leakage • Use the framework to embed mitigation into each phase: pretraining, fine-tuning, inference, RAG, feedback For Users & Deployers: • Don’t treat LLMs as black boxes. Ask if data is stored, recalled, or used to retrain • Evaluate vendor claims with structured questions from the report • Build internal governance that tracks model behaviors over time For Decision-Makers & Risk Owners: • Use this to complement your DPIAs with LLM-specific threat modeling • Shift privacy thinking from legal compliance to architectural accountability • Set organizational standards for “commercial-safe” LLM usage This isn’t about slowing innovation. It’s about future-proofing it. Because the next phase of AI scale won’t just be powered by better models. It will be constrained and enabled by how seriously we engineer for trust. Thanks European Data Protection Board, Isabel Barberá H/T Peter Slattery, PhD

Generative AI offers transformative potential, but how do we harness it without compromising crucial data privacy? It's not an afterthought — it's central to the strategy. Evaluating the right approach depends heavily on specific privacy goals and data sensitivity. One starting point, with strong vendor contracts, is using the LLM context window directly. For larger datasets, Retrieval-Augmented Generation (RAG) scales well. RAG retrieves relevant information at query time to augment the prompt, which helps keep private data out of the LLM's core training dataset. However, optimizing RAG across diverse content types and meeting user expectations for structured, precise answers can be challenging. At the other extreme lies Self-Hosting LLMs. This offers maximum control but introduces significant deployment and maintenance overhead, especially when aiming for the capabilities of large foundation models. For ultra-sensitive use cases, this might be the only viable path. Distilling larger models for specific tasks can mitigate some deployment complexity, but the core challenges of self-hosting remain. Look at Apple Intelligence as a prime example. Their strategy prioritizes user privacy through On-Device Processing, minimizing external data access. While not explicitly labeled RAG, the architecture — with its semantic index, orchestration, and LLM interaction — strongly resembles a sophisticated RAG system, proving privacy and capability can coexist. At Egnyte, we believe robust AI solutions must uphold data security. For us, data privacy and fine-grained, authorized access aren't just compliance hurdles; they are innovation drivers. Looking ahead to advanced Agent-to-Agent AI interactions, this becomes even more critical. Autonomous agents require a bedrock of trust, built on rigorous access controls and privacy-centric design, to interact securely and effectively. This foundation is essential for unlocking AI's future potential responsibly.

𝐓𝐡𝐞 𝐔.𝐒. 𝐢𝐧𝐭𝐞𝐥𝐥𝐢𝐠𝐞𝐧𝐜𝐞 𝐜𝐨𝐦𝐦𝐮𝐧𝐢𝐭𝐲 𝐢𝐬 𝐛𝐮𝐢𝐥𝐝𝐢𝐧𝐠 𝐚 𝐨𝐧𝐞-𝐬𝐭𝐨𝐩 𝐝𝐚𝐭𝐚 𝐦𝐚𝐫𝐤𝐞𝐭𝐩𝐥𝐚𝐜𝐞 𝐚𝐧𝐝 𝐲𝐨𝐮𝐫 𝐜𝐮𝐬𝐭𝐨𝐦𝐞𝐫𝐬' 𝐦𝐨𝐬𝐭 𝐬𝐞𝐧𝐬𝐢𝐭𝐢𝐯𝐞 𝐝𝐚𝐭𝐚 𝐢𝐬 𝐟𝐨𝐫 𝐬𝐚𝐥𝐞. As reported by The Intercept, spy agencies are creating a centralized platform to buy and analyze commercially available data, everything from location pings and biometric info to sentiment analysis and behavioral patterns using AI. Here’s what every business leader needs to take seriously: 𝐏𝐫𝐢𝐯𝐚𝐜𝐲 𝐛𝐞𝐜𝐨𝐦𝐞𝐬 𝐚 𝐬𝐭𝐫𝐚𝐭𝐞𝐠𝐢𝐜 𝐫𝐢𝐬𝐤. ↳ You don’t need to be in defense or surveillance to feel the impact. If your app, ad tech stack, or data partnerships funnel info into these broker networks, your customers are already exposed and so is your brand. AI makes this exponentially more powerful. ↳ These tools aren’t just mining data, they’re predicting behavior, inferring intent, and drawing conclusions with no transparency and no due process. The ethical stakes for any business using AI just got higher. Founders and executives must treat data governance as brand governance. ↳ This goes beyond ticking boxes, it's about earning trust, protecting your reputation, and securing your business’s future in a world where every click, every location, and every action can be tracked or misused by invisible actors. We’re in a moment where business strategy, ethics, and AI policy are colliding fast. Are you ready for that future? To strategically navigate these challenges and embed human-centered AI into your organization’s DNA, fostering ethical decision-making and resilient leadership, let’s connect. I partner with leaders and teams to build forward-thinking strategies that safeguard trust and drive sustainable business growth. https://lnkd.in/g37-HCRX + Follow Jeff Eyet 🔑✨ for more insights on AI and Business Strategy. #JeffEyet #TheBerkeleyInnovationGroup #AILeadership #DataEthics #DigitalStrategy #FutureOfWork #Privacy #SurveillanceCapitalism #Founders #BusinessStrategy #ResponsibleAI

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".

AI is now being taught to forget. No, seriously. "Machine unlearning" is the hottest new tech trend. The same companies that spent billions teaching AI to remember everything are now spending billions teaching it selective amnesia. Why? Because GDPR says users have the "right to be forgotten." So now we have AI models that can surgically remove specific memories while keeping everything else intact. Think about the absurdity: • We want AI to be smarter than humans • But also to forget on command • Like giving a supercomputer alzheimer's by design The real kicker? Retraining an entire model costs millions. So companies are building "digital lobotomy" tools instead. Here's what nobody's talking about: - What happens when AI "forgets" safety training? - Who decides what deserves to be forgotten? - Can AI forget its own biases? Or just the evidence of them? We went from "AI will remember everything forever" to "AI, please forget my embarrassing 2019 data" in record time. Plot twist: The same forgetting tech that protects your privacy could help companies hide inconvenient truths. "Sorry, our AI forgot about those failed safety tests." The future of AI isn't just about what it learns. It's about what it chooses to forget. #AIEthics #MachineLearning #Privacy #TechTrends

Data privacy and ethics must be a part of data strategies to set up for AI. Alignment and transparency are the most effective solutions. Both must be part of product design from day 1. Myths: Customers won’t share data if we’re transparent about how we gather it, and aligning with customer intent means less revenue. Instacart customers search for milk and see an ad for milk. Ads are more effective when they are closer to a customer’s intent to buy. Instacart charges more, so the app isn’t flooded with ads. SAP added a data gathering opt-in clause to its contracts. Over 25,000 customers opted in. The anonymized data trained models that improved the platform’s features. Customers benefit, and SAP attracts new customers with AI-supported features. I’ve seen the benefits first-hand working on data and AI products. I use a recruiting app project as an example in my courses. We gathered data about the resumes recruiters selected for phone interviews and those they rejected. Rerunning the matching after 5 select/reject examples made immediate improvements to the candidate ranking results. They asked for more transparency into the terms used for matching, and we showed them everything. We introduced the ability to reject terms or add their own. The 2nd pass matches improved dramatically. We got training data to make the models better out of the box, and they were able to find high-quality candidates faster. Alignment and transparency are core tenets of data strategy and are the foundations of an ethical AI strategy. #DataStrategy #AIStrategy #DataScience #Ethics #DataEngineering