Benefits of Synthetic Data for Privacy Protection

Anonymized data makes it safe, fast, and easy to start data and ML projects Each phase of model development should be evaluated as a separate use case for determining the "minimum necessary" amount of personal information required. If we crudely break up model development into these five use cases: - Exploratory data analysis and feature selection - Model training - Model testing - Model inference in production - Model performance monitoring The first three require NO processing of personal information and can be performed using anonymized synthetic data. Therefore, data minimization requires the use of anonymous data in these phases. Creating this default reduces the cost of experimentation and allows idea validation to happen more quickly. The final two steps *require* the use personal information to create meaningful outcomes (predicting the behavior of fictional synthetic users is useless). Since processing personal data is *necessary* at this step, secure processing and confidential computing approaches allow strong protections in these phases. There's no silver bullet to "solve" privacy for all use cases, so it's important to think about the unique requirements of each stage of a project. And over time, organizations can replace one-off "minimum necessary" analysis with purpose-built technical infrastructure to enable the responsible use of data at each phase.

𝗡𝗲𝘄 𝗿𝗲𝘀𝗲𝗮𝗿𝗰𝗵 𝗽𝘂𝗯𝗹𝗶𝘀𝗵𝗲𝗱! Medical imaging is packed with hidden clinical biomarkers, but privacy hurdles and data scarcity often keep this treasure trove locked away from AI innovation. Frustrating, right? That’s exactly what inspired me and Abdullah Hosseini to ask: Can we generate synthetic medical images that not only look real, but also preserve the critical biomarkers clinicians rely on? So, we dove in. Using cutting-edge diffusion models fused with Swin-transformer networks, we generated synthetic images across three modalities—radiology (chest X-rays), ophthalmology (OCT), and histopathology (breast cancer slides). The big question: 𝗗𝗼 𝘁𝗵𝗲𝘀𝗲 𝘀𝘆𝗻𝘁𝗵𝗲𝘁𝗶𝗰 𝗶𝗺𝗮𝗴𝗲𝘀 𝗸𝗲𝗲𝗽 𝘁𝗵𝗲 𝘀𝘂𝗯𝘁𝗹𝗲, 𝗱𝗶𝘀𝗲𝗮𝘀𝗲-𝗱𝗲𝗳𝗶𝗻𝗶𝗻𝗴 𝗳𝗲𝗮𝘁𝘂𝗿𝗲𝘀 𝗶𝗻𝘁𝗮𝗰𝘁? • Our diffusion models faithfully preserved key biomarkers—like lung markings in X-rays and retinal abnormalities in OCT—across all datasets. • Classifiers trained only on synthetic data performed nearly as well as those trained on real images, with F1 and AUC scores hitting 0.8–0.99. • No statistically significant difference in diagnostic performance—meaning synthetic data could stand in for real data in many AI tasks, while protecting patient privacy. This work shows synthetic data isn’t just a lookalike—it’s a powerful, privacy-preserving tool for research, clinical AI, and education. Imagine sharing and scaling medical data without the headaches of privacy risk or limited access! Read the full paper: https://lnkd.in/eW6TM9H2 Get the code & datasets: https://lnkd.in/ek4wSkg3 #AI #Innovation #SyntheticData #DiffusionModels #MedicalImaging #HealthcareInnovation #DigitalHealth #Frontiers #WeillCornell #HealthTech #HealthcareAI #PrivacyPreservingAI #GenerativeAI #Biomarkers #MachineLearning #Qatar #MENA #MiddleEast #NorthAfrica #MENAIRegion #MENAInnovation #UAE #UnitedArabEmirates #SaudiArabia #KSA #Egypt AI Innovation Lab Weill Cornell Medicine Weill Cornell Medicine - Qatar Cornell Tech Cornell University

🚀 Generating High-Quality Synthetic Data — While Preserving Feature Relationships In today’s data-driven world, organizations urgently need realistic data for testing, development, and AI training—but privacy concerns and regulations like HIPAA and FERPA often make using real data impossible. That's where structured synthetic data comes in. Harpreet Singh and I developed a synthetic data generation pipeline that not only mimics the distribution of real data—but also preserves the relationships between features, something many approaches overlook. 🧠 Here's a look at what sets this approach apart: ✅ Preprocessing - Imputes missing values (median/mode/“Unknown”) - Encodes categoricals smartly: binary, one-hot, or frequency-based - Fixes skewed features using Box-Cox - Standardizes numerical data - Stores all parameters for full reversibility 🔍 Clustering with HDBSCAN Real data often comes from diverse subgroups (e.g., customer segments or patient cohorts). Using HDBSCAN, we automatically detect natural clusters without predefining their number. This ensures minority patterns aren’t averaged out. 📊 Per-Cluster Modeling Using Copulas Each cluster is modeled independently to capture local behavior. - First, we fit the best marginal distribution for each feature (normal, log-normal, gamma, etc.) - Then, using copulas (Gaussian, Student-T, Clayton), we preserve the inter-feature dependencies—ensuring we don’t just get realistic individual values, but also realistic combinations This step is crucial. It avoids scenarios like low-income customers buying large numbers of luxury items—something that happens when relationships aren't preserved. 🎯 Generation and Postprocessing - Samples are drawn from the fitted copula - Inverse CDF restores each feature’s shape - Reverse standardization and decoding returns everything to the original format - Categorical encodings are fully recovered (binary, one-hot, frequency) 🧪 Validation The pipeline doesn't stop at generation—it rigorously validates: - Kolmogorov-Smirnov and chi-square tests for distributions - Correlation matrix comparison (Pearson, Spearman) - Frobenius norms for dependency structure accuracy - Cluster proportion alignment ⚠️ Limitations: All variables are treated as continuous during dependency modeling—so while relationships are preserved broadly, some nuanced categorical interactions may be less precise. ✅ Use Cases: - Safe test data for dev teams - Realistic ML training data - Simulating rare edge cases - Privacy-preserving analysis in finance, health, and retail 📚 Full breakdown with code is here: 👉 https://lnkd.in/gS5a3Sk7 Let us know what you think—or if you'd like help implementing something similar for your team. If you find it useful, don't shy away from liking or reposting it. #SyntheticData #Privacy #AI #MachineLearning #DataScience #Copulas #HDBSCAN #DataEngineering

🤖 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. 👇

📒 Why Synthetic Data Could Be the Game-Changer for AI in Mental Health Care In mental health AI, data is the foundation upon which we build intelligent, empathetic, and effective models. Traditionally, real-world therapy session data has been the gold standard—but what if synthetic data could not only match it but exceed its potential? At Urban Health AI, we’ve been exploring the use of synthetic data to train Noah, our AI-powered mental health coach, and the results are remarkable. Here’s why synthetic data might be the future of mental health innovation: 1️⃣ Quality Over Quantity Real-world therapy data is often messy, incomplete, and inconsistent. It comes with challenges like varying session quality, subjective interpretations, and bias introduced by individual therapists. Synthetic data, on the other hand, is purpose-built. It’s designed to reflect the best practices in psychotherapy, incorporating evidence-based techniques like CBT, ACT, and DBT in their purest forms. This ensures the highest quality training material for AI models. 2️⃣ Diversity and Scalability Real-world data is limited by the diversity of patient populations, cultural contexts, and therapeutic approaches it represents. Synthetic data can generate endless variations, simulating scenarios that are rare or underrepresented in real-world therapy sessions. • What happens if a patient hesitates? • What if the therapist explores a less common but effective technique? • How should an AI respond to conflicting emotional cues? With synthetic data, we can train AI to handle a broader range of situations with nuanced precision. 3️⃣ Ethical and Privacy-First Approach Using real-world therapy session data often involves significant ethical and privacy challenges. Patient confidentiality must always come first, which limits the availability of comprehensive datasets. Synthetic data eliminates this concern. By generating high-quality, anonymized datasets, we can train AI without compromising anyone’s privacy or breaching ethical boundaries. 4️⃣ Accelerated Innovation Through Feedback Loops Synthetic data isn’t static. It can be vetted, tested, and iterated upon in a controlled environment, creating a continuous feedback loop between data generation and AI performance. 💬 What are your thoughts on the use of synthetic data in mental health AI? #MentalHealth #AIInnovation #SyntheticData #BehavioralHealth #UrbanHealthAI #Noah