Demonstrating Results While Maintaining Data Privacy

Small Models, Big Knowledge: How DRAG Bridges the AI Efficiency-Accuracy Gap 👉 Why This Matters Modern AI systems face a critical tension: large language models (LLMs) deliver impressive knowledge recall but demand massive computational resources, while smaller models (SLMs) struggle with factual accuracy and "hallucinations." Traditional retrieval-augmented generation (RAG) systems amplify this problem by requiring constant updates to vast knowledge bases. 👉 The Innovation DRAG introduces a novel distillation framework that transfers RAG capabilities from LLMs to SLMs through two key mechanisms: 1. Evidence-based distillation: Filters and ranks factual snippets from teacher LLMs 2. Graph-based structuring: Converts retrieved knowledge into relational graphs to preserve critical connections This dual approach reduces model size requirements by 10-100x while improving factual accuracy by up to 27.7% compared to prior methods like MiniRAG. 👉 How It Works 1. Evidence generation: A large teacher LLM produces multiple context-relevant facts 2. Semantic filtering: Combines cosine similarity and LLM scoring to retain top evidence 3. Knowledge graph creation: Extracts entity relationships to form structured context 4. Distilled inference: SLMs generate answers using both filtered text and graph data The process mimics how humans combine raw information with conceptual understanding, enabling smaller models to "think" like their larger counterparts without the computational overhead. 👉 Privacy Bonus DRAG adds a privacy layer by: - Local query sanitization before cloud processing - Returning only de-identified knowledge graphs Tests show 95.7% reduction in potential personal data leakage while maintaining answer quality. 👉 Why It’s Significant This work addresses three critical challenges simultaneously: - Makes advanced RAG capabilities accessible on edge devices - Reduces hallucination rates through structured knowledge grounding - Preserves user privacy in cloud-based AI interactions The GitHub repository provides full implementation details, enabling immediate application in domains like healthcare diagnostics, legal analysis, and educational tools where accuracy and efficiency are non-negotiable.

This paper investigates the application of privacy-preserving open-weights LLMs for extracting structured information from free-text radiology reports and compares their performance with rule-based systems and closed-weights models like OpenAI's GPT-4o. 1️⃣ Open-weights LLMs demonstrated superior zero-shot performance compared to rule-based systems (e.g., CheXpert with a macro-averaged F1 score of 73.1%) and were comparable to GPT-4o (92.4%) on English datasets. 2️⃣ On nonpublic German datasets, open-weights LLMs also outperformed rule-based systems, particularly for complex and variable descriptive tasks, with Mistral-Large achieving 91.6% in F1 score. 3️⃣ Fine-tuning open-weights LLMs using as few as 1000 annotated reports surpassed the performance of BERT (86.7%), with models like Mistral-Large reaching 94.3%. 4️⃣ Local fine-tuning and deployment on secure clinical infrastructure provide significant advantages for data privacy, bypassing regulatory complexities associated with external servers. 5️⃣ The study highlights the scalability of open-weights LLMs, advocating their potential for broader clinical applications while noting higher computational demands compared to lightweight models like BERT. ✍🏻 Sebastian Nowak, Benjamin Wulff, Yannik C. Layer, Maike Theis, Alexander Isaak, Dr. med. Babak Salam, Wolfgang Block, Daniel Kütting, Claus C. Pieper, Julian A. Luetkens, Univ.-Prof. Dr. Ulrike Attenberger, Alois M. Sprinkart. Privacy-ensuring Open-weights Large Language Models Are Competitive with Closed-weights GPT-4o in Extracting Chest Radiography Findings from Free-Text Reports. Radiology. 2025. DOI: 10.1148/radiol.240895

Day 7 of #30DaysOfFLCode with OpenMined: Structured Transparency continues to amaze me, particularly in how it transforms industries like healthcare, from academic research and beyond. Imagine working with large sensitive medical datasets but facing strict legal and ethical restrictions that limit how these datasets can be accessed, shared, or analyzed. This is where components of Structured Transparency, specifically Privacy-Enhancing Technologies (PETs), help to bridge the gap. Tools like Federated Learning (FL), Differential Privacy (DP), and Secure Multi-Party Computation (SMPC), either solely or combined, are enabling groundbreaking advancements while addressing privacy and ethical concerns. During the COVID-19 pandemic, several organizations, including Google, Apple, and NVIDIA, contributed to advancing privacy-preserving technologies for critical use cases. Take the case of the EXAM Model (https://lnkd.in/dv67rSwd): a Federated Learning approach that allowed 20 institutions worldwide to collaboratively develop an AI model to predict oxygen needs in COVID-19 patients, without ever sharing sensitive patient data. This approach not only maintained patient privacy but also achieved impressive accuracy in forecasting outcomes while improving the model's adaptability across diverse datasets by 38% compared to models trained at a single institution using that institution's data. FL showcased how global collaboration can drive innovation responsibly, even under strict data privacy constraints. This is just one example, but it highlights how PETs transform sensitive, high-stakes environments like healthcare. They allow researchers to analyze data securely and responsibly without needing direct access, protecting individual privacy while advancing life-saving innovations. And here’s the thing: you don’t have to be a technical expert to appreciate or use these tools. Just like you don’t need to build AI models from scratch to benefit from AI-powered solutions, non-technical professionals, including lawyers and other privacy advocates, can meaningfully contribute to the adoption and application of PETs. I’m particularly intrigued by the legal and ethical implications that arise with the use of PETs, thence my question yesterday. After all, the overarching goal of these technologies is to ensure compliance with privacy laws and ethical policies.

For companies that have strict data locality and compliance requirements, the ability to secure PII during data replication is crucial. A few ways that companies can handle PII effectively when it comes to data replication: 1️⃣ Column Exclusion: safeguard sensitive information by excluding specific columns from replication entirely, ensuring that they do not appear in the data warehouse or lake for downstream consumption. 2️⃣ Column Allowlist: utilize an allowlist to ensure only non-sensitive, pre-approved columns are replicated, minimizing the risk of exposing sensitive data. 3️⃣ Column Hashing: obfuscating sensitive PII into a hashed format, maintaining privacy while allowing for activity tracking and data analysis without actual data exposure. 4️⃣ Column Encryption: encrypt PII before replication to ensure that data is secure both in transit and at rest, accessible only via decryption keys. 5️⃣ Audit Trails: implement comprehensive logging to track changes to replicated data, which is essential for monitoring, compliance, and security investigations. 6️⃣ Geofencing: control data replication based on geographic boundaries to comply with laws like GDPR, which restricts cross-border data transfers. By integrating these strategies, companies can comply with strict data protection regulations and enhance their reputation by demonstrating a commitment to data security. 🔒 One of our customers is a B2C fintech platform. They use Artie (YC S23) to replicate customer and transaction data across platforms to analyze and monitor changes in risk scores. To ensure compliance with financial regulations and safeguard customer data, the company uses column hashing for sensitive financial details and customer identifiers. This way, they are able to identify important PII changes without exposing sensitive data to their analysts. Additionally, they implemented audit trails (our history mode/SCD tables!) to monitor and log all data changes. Geofencing is utilized to restrict data processing to specific regions, to remain compliant with regulations like GDPR. How is your organization managing PII in data replication? Are there other strategies you find effective? #dataengineering #datareplication #data

In healthcare leveraging sensitive data for AI and ML is crucial, but privacy concerns often hinder progress. In this walkthrough we outline a step-by-step guide to generating high-quality, privacy-safe synthetic patient data that maintains utility while preserving patient confidentiality. We get into how to protect against privacy attacks, and demonstrate how to work with complex, multi-modal health data including numeric values, categorical information, free text, and time-series data. This approach goes beyond simple anonymization, creating new records not based on any single individual.