Privacy-Preserving Encryption Models

𝗪𝗵𝗮𝘁 𝗜𝗳 𝗬𝗼𝘂𝗿 𝗗𝗮𝘁𝗮 𝗖𝗼𝘂𝗹𝗱 𝗦𝘁𝗮𝘆 𝗘𝗻𝗰𝗿𝘆𝗽𝘁𝗲𝗱 𝗪𝗵𝗶𝗹𝗲 𝗦𝘁𝗶𝗹𝗹 𝗕𝗲𝗶𝗻𝗴 𝗖𝗼𝗺𝗽𝘂𝘁𝗲𝗱 𝗢𝗻? Enter Homomorphic Encryption.  As organizations strive to balance data security with usability, homomorphic encryption emerges as a groundbreaking solution. It allows computations to be performed directly on encrypted data—without ever needing to decrypt it. This ensures sensitive data remains secure at all times, whether at rest, in transit, or during processing. 𝗕𝗲𝗻𝗲𝗳𝗶𝘁𝘀 𝗼𝗳 𝗛𝗼𝗺𝗼𝗺𝗼𝗿𝗽𝗵𝗶𝗰 𝗘𝗻𝗰𝗿𝘆𝗽𝘁𝗶𝗼𝗻   - Always Encrypted: Data remains encrypted throughout its lifecycle, drastically reducing the risk of exposure.  - Privacy Meets Usability: No longer must organizations choose between protecting data and making it functional for analysis.  - Regulatory Compliance: Simplifies adherence to privacy laws like GDPR and HIPAA.  - Cloud Adoption: Offers a secure path for migrating to and leveraging the cloud.  - Critical for Sensitive Industries: Especially impactful for sectors like healthcare, where privacy is paramount.  𝗧𝘆𝗽𝗲𝘀 𝗼𝗳 𝗛𝗼𝗺𝗼𝗺𝗼𝗿𝗽𝗵𝗶𝗰 𝗘𝗻𝗰𝗿𝘆𝗽𝘁𝗶𝗼𝗻   𝟭. 𝗙𝘂𝗹𝗹𝘆 𝗛𝗼𝗺𝗼𝗺𝗼𝗿𝗽𝗵𝗶𝗰 𝗘𝗻𝗰𝗿𝘆𝗽𝘁𝗶𝗼𝗻 (𝗙𝗛𝗘):     The gold standard, FHE allows unlimited computations on encrypted data. Its applications in cloud computing, AI, and beyond make it a powerful tool for organizations prioritizing both security and flexibility.      𝟮. 𝗣𝗮𝗿𝘁𝗶𝗮𝗹 𝗛𝗼𝗺𝗼𝗺𝗼𝗿𝗽𝗵𝗶𝗰 𝗘𝗻𝗰𝗿𝘆𝗽𝘁𝗶𝗼𝗻 (𝗣𝗛𝗘):     Enables certain computations while maintaining encryption. It’s faster and more efficient but limited in scope compared to FHE.  𝟯. 𝗦𝗼𝗺𝗲𝘄𝗵𝗮𝘁 𝗛𝗼𝗺𝗼𝗺𝗼𝗿𝗽𝗵𝗶𝗰 𝗘𝗻𝗰𝗿𝘆𝗽𝘁𝗶𝗼𝗻 (𝗦𝗛𝗘):     A middle ground, SHE supports limited operations and is often used for specific applications where only partial data processing is needed.  𝟰. 𝗟𝗲𝘃𝗲𝗹𝗹𝗲𝗱 𝗙𝘂𝗹𝗹𝘆 𝗛𝗼𝗺𝗼𝗺𝗼𝗿𝗽𝗵𝗶𝗰 𝗘𝗻𝗰𝗿𝘆𝗽𝘁𝗶𝗼𝗻 (𝗟𝗛𝗘):     Supports computations of bounded depth, striking a balance between functionality and performance.  𝗖𝗵𝗮𝗹𝗹𝗲𝗻𝗴𝗲𝘀 𝘁𝗼 𝗖𝗼𝗻𝘀𝗶𝗱𝗲𝗿   - Performance: Homomorphic encryption is still slower than traditional methods, but advancements are being made to optimize its speed.   - Complex Integration: It often requires adjustments to applications or custom-built programs, adding complexity to implementation.  𝗧𝗵𝗲 𝗙𝘂𝘁𝘂𝗿𝗲 𝗼𝗳 𝗛𝗼𝗺𝗼𝗺𝗼𝗿𝗽𝗵𝗶𝗰 𝗘𝗻𝗰𝗿𝘆𝗽𝘁𝗶𝗼𝗻   As technology evolves, this methodology is becoming more accessible and efficient. Forward-thinking businesses are already exploring its potential to revolutionize secure data processing.  How is your organization leveraging encryption to secure data? Are you considering homomorphic encryption as part of your cybersecurity strategy? Share your thoughts or ask questions below!  #CyberSecurity #DataPrivacy #HomomorphicEncryption #CloudSecurity #Innovation #RegulatoryCompliance  

🚨 Big breakthrough in AI + Privacy 🚨 We all know large language models (LLMs) are trained on tons of data - sometimes that data may include personal information. The question is: what stops bad actors from extracting it? That’s where Differential Privacy (DP) comes in. Think of DP as adding carefully calibrated “noise” during training so that no single user’s data can overly influence the model. In simple terms: the model learns patterns, not people. 💡 How DP is implemented? - Here are a few ways,  • Noise Injection: Adds random noise during training.  • Memorization Prevention: Stops the model from memorizing personal details.  • Privacy Guarantees: Provides mathematical proof of protection. Recent advances go even further,  • User-Level DP: Protects each individual, even if they contribute lots of data.  • New Frameworks: More accurate tools for measuring privacy (like Edgeworth accountants). 👉 And now the exciting part: Google AI has released VaultGemma - capable open model (1B parameters) trained from scratch with full Differential Privacy. 𝗨𝗻𝗹𝗶𝗸𝗲 𝗺𝗮𝗻𝘆 𝗺𝗼𝗱𝗲𝗹𝘀 𝘁𝗵𝗮𝘁 𝗼𝗻𝗹𝘆 𝗮𝗽𝗽𝗹𝘆 𝗗𝗣 𝗱𝘂𝗿𝗶𝗻𝗴 𝗳𝗶𝗻𝗲-𝘁𝘂𝗻𝗶𝗻𝗴, 𝗩𝗮𝘂𝗹𝘁𝗚𝗲𝗺𝗺𝗮 𝗲𝗻𝗳𝗼𝗿𝗰𝗲𝘀 𝗽𝗿𝗶𝘃𝗮𝗰𝘆 𝗿𝗶𝗴𝗵𝘁 𝗳𝗿𝗼𝗺 𝗽𝗿𝗲𝘁𝗿𝗮𝗶𝗻𝗶𝗻𝗴. How it was done? ✅DP-SGD (Differentially Private Stochastic Gradient Descent) with gradient clipping + Gaussian noise. ✅Built on JAX Privacy (Google’s open-source library for scalable private ML). ✅Key optimizations for scale:   • Vectorized per-example clipping.  • Gradient accumulation for large batches.  • Truncated Poisson subsampling for efficient sampling. Result: VaultGemma achieved a strong DP guarantee of (ε ≤ 2.0, δ ≤ 1.1e−10) at the sequence level (1024 tokens). ⚖️ Yes, there’s still a small utility gap compared to non-private models. But the fact that Google pulled off private pretraining proves something huge. We can build AI models that are both powerful AND privacy-preserving. This sets the tone for the future of safe, transparent, and trustworthy AI.

Pattern Labs and Anthropic have published a highly detailed technical paper outlining how to protect both user data and model IP during AI inference using Trusted Execution Environments (TEEs). If you are building or deploying GenAI in sensitive environments, this report is essential. Key takeaways: • Describes two confidentiality models: protecting model inputs and outputs, and protecting model weights and architecture • Explains how TEEs provide security through hardware-enforced isolation and cryptographic attestation • Covers implementations across AWS Nitro Enclaves, Azure Confidential VMs, and GCP Confidential Space • Examines support for AI accelerators such as NVIDIA H100 using either native or bridged TEE approaches • Provides analysis of over 30 risks including KMS misconfiguration, supply chain compromise, and insecure enclave provisioning Who should care: • Cloud AI service providers offering inference APIs • Enterprises using LLMs to process sensitive or regulated data • Model owners deploying high-risk or frontier models with SL4 or SL5 confidentiality requirements What stood out: • Practical coverage of Bring Your Own Vulnerable Enclave (BYOVE) risks • Focus on reproducible builds and open-source auditability to ensure enclave integrity • Clear guidance on KMS design, model provisioning, and runtime isolation to prevent data leakage One action item: Use this report as a design and threat modeling checklist for any confidential inference deployment. Start by securing your enclave build process and verifying the trust chain of your model provisioning workflow. #ConfidentialComputing #GenAI #AIInference #LLMSecurity #TrustedExecution #ModelProtection #AIPrivacy #Anthropic #PatternLabs #SecureInference #ZeroTrust #CloudSecurity

AI reaches a milestone: privacy by design at scale Google AI and DeepMind have announced VaultGemma, a 1B parameter, open-weight model trained entirely with differential privacy (DP). Why does this matter? Most large LLMs carry inherent privacy risks: they can memorise and reproduce fragments of their training data. A serious issue if it’s a patient record, bank detail, or private correspondence. VaultGemma's training method - DP-SGD, which limits how much influence any datapoint has and adds noise to blur details - ensures no single personal data included in the training could later be exposed. The result: a mathematical guarantee of privacy, the strongest ever achieved at this scale. The opportunities In healthcare, finance, and government, the implications are immediate: 🔸 Hospitals can analyse patient data without risking disclosure. 🔸 Banks can detect fraud or assess credit risk within GDPR rules. 🔸 Governments can train models on citizen data while meeting privacy-by-design requirements. In each case, sensitive data shifts from a liability to an asset that can drive innovation. The challenges 1️⃣ Performance: VaultGemma is less accurate than the frontier LLMs, closer to the performance of GPT-3.5. This is the cost of stronger privacy: trading short-term capability for long-term protection. 2️⃣ Jurisdiction: The model guarantees privacy, but not sovereignty. Built by an American provider, it remains subject to U.S. law. Under the CLOUD Act, American authorities can compel access even to data hosted abroad. How this compares 💠 Gemini has strong capability and multimodality, but privacy protections rest on corporate policy. 💠 ChatGPT-5 leads in performance, but is closed & under U.S. jurisdiction. 💠 Claude is positioned as “safety-first,” yet its privacy controls are policy-based, not mathematical. By contrast, VaultGemma offers provable privacy. The trade-off is weaker performance and continued U.S. jurisdiction - but it moves the conversation from “trust us” to “prove it.” Leaders have now a wider choice for adopting AI: ✔️ Privacy-first model: trade accuracy for provable privacy. Suited for highly regulated sectors and SMEs needing compliance. Lower cost, limited customisation, under U.S. law. ✔️ Frontier LLMs: cutting-edge capability at scale. Privacy rests on policy, with jurisdiction split - U.S., Chinese, or EU law. Highest-priced via usage-based APIs, but with the broadest ecosystems and integrations. ✔️ Sovereign alternatives: slower today, but with greater control of data and law. Could adopt privacy-by-design methods like VaultGemma, though requiring heavy upfront investment. Higher initial cost, offset by customisation and long-term resilience. AI has reached a milestone: privacy by design is possible at scale. Leaders need to balance trust, compliance, performance, and control in their choices. #AI #ResponsibleAI #DataPrivacy #DigitalSovereignty #Boardroom

Today, National Institute of Standards and Technology (NIST) published its finalized Guidelines for Evaluating ‘Differential Privacy’ Guarantees to De-Identify Data (NIST Special Publication 800-226), a very important publication in the field of privacy-preserving machine learning (PPML). See: https://lnkd.in/gkiv-eCQ The Guidelines aim to assist organizations in making the most of differential privacy, a technology that has been increasingly utilized to protect individual privacy while still allowing for valuable insights to be drawn from large datasets. They cover: I. Introduction to Differential Privacy (DP): - De-Identification and Re-Identification: Discusses how DP helps prevent the identification of individuals from aggregated data sets. - Unique Elements of DP: Explains what sets DP apart from other privacy-enhancing technologies. - Differential Privacy in the U.S. Federal Regulatory Landscape: Reviews how DP interacts with existing U.S. data protection laws. II. Core Concepts of Differential Privacy: - Differential Privacy Guarantee: Describes the foundational promise of DP, which is to provide a quantifiable level of privacy by adding statistical noise to data. - Mathematics and Properties of Differential Privacy: Outlines the mathematical underpinnings and key properties that ensure privacy. - Privacy Parameter ε (Epsilon): Explains the role of the privacy parameter in controlling the level of privacy versus data usability. - Variants and Units of Privacy: Discusses different forms of DP and how privacy is measured and applied to data units. III. Implementation and Practical Considerations: - Differentially Private Algorithms: Covers basic mechanisms like noise addition and their common elements used in creating differentially private data queries. - Utility and Accuracy: Discusses the trade-off between maintaining data usefulness and ensuring privacy. - Bias: Addresses potential biases that can arise in differentially private data processing. - Types of Data Queries: Details how different types of data queries (counting, summation, average, min/max) are handled under DP. IV. Advanced Topics and Deployment: - Machine Learning and Synthetic Data: Explores how DP is applied in ML and the generation of synthetic data. - Unstructured Data: Discusses challenges and strategies for applying DP to unstructured data. - Deploying Differential Privacy: Provides guidance on different models of trust and query handling, as well as potential implementation challenges. - Data Security and Access Control: Offers strategies for securing data and controlling access when implementing DP. V. Auditing and Empirical Measures: - Evaluating Differential Privacy: Details how organizations can audit and measure the effectiveness and real-world impact of DP implementations. Authors: Joseph Near David Darais Naomi Lefkovitz Gary Howarth, PhD

🔬 Towards Decentralized and Privacy-Preserving Clinical Trials 🧠💡Register, learn and build Decentralization in clinical research is not just about scalability or cost-efficiency. It’s a cryptographic transformation that redefines trust and data sovereignty in medical innovation. Technologies like Zero-Knowledge Proofs (ZKPs) and Fully Homomorphic Encryption (FHE) are enabling a new paradigm in decentralized trials: ✅ Privacy without compromising verification: With ZKPs, patients can prove eligibility (inclusion/exclusion criteria) without revealing their full medical history. Compliance is validated without exposing sensitive data. ✅ Computation over encrypted data (FHE): FHE allows researchers to run statistical analyses and predictive models directly on encrypted datasets. No need to decrypt—privacy is preserved even during processing. Ideal for multicenter trials or pharmacogenomic studies. ✅ Traceability without surveillance: Combining blockchain with ZK/FHE enables immutable and auditable recording of clinical events (dosage, adverse effects, outcomes) without identifying the patient. 🌐 In this new model: Data stays where it’s generated (edge computing, patient devices) No centralized data hoarding or exposure risks GDPR and similar regulations are met by design, not workaround 📣 If you're working at the intersection of digital health, cryptography and clinical innovation, this is the future: crypto-technology powering secure, precise, and ethical research. #ZKProofs #FHE #DeSci #DecentralizedTrials #PrivacyByDesign #Web3Health #DigitalTrust #Blockchain #ClinicalResearch #HealthTech Anthony Joaquim José Daniel Dr. Hidenori Vivek Helena Lars Yousuke Carlos Iker Paris João Domingos

This paper presents the LLM-Anonymizer, an open-source tool that uses locally deployed LLMs to deidentify medical documents while preserving essential clinical information. 1️⃣ High Anonymization Accuracy: The LLM-Anonymizer, particularly with Llama-3 70B, achieved a 99.24% success rate in removing personal identifiers, with only a 0.76% false-negative rate. 2️⃣ Benchmarking Local LLMs: Eight LLMs (e.g., Llama-3, Llama-2, Mistral, and Phi-3 Mini) were tested on 250 German clinical letters, with Llama-3 70B performing best. 3️⃣ Comparison With Existing Tools: The LLM-Anonymizer outperformed CliniDeID and Microsoft’s Presidio in sensitivity and accuracy for redacting personal identifiers. 4️⃣ Privacy-Preserving and Open Source: The tool runs on local hardware, ensuring data privacy, and is available on GitHub for public use. 5️⃣ User-Friendly Interface: A browser-based interface simplifies document anonymization without requiring programming skills. 6️⃣ Regulatory Considerations: The tool aligns with GDPR standards for anonymization but is not fully HIPAA-compliant. ✍🏻 Isabella Wiest, Marie-Elisabeth Leßmann, Fabian Wolf, Dyke Ferber, Marko Van Treeck, Jiefu Zhu, Matthias Ebert, Christoph Benedikt Westphalen, Martin Wermke, Jakob Nikolas Kather. Deidentifying Medical Documents with Local, Privacy-Preserving Large Language Models: The LLM-Anonymizer. NEJM AI. 2025. DOI: 10.1056/AIdbp2400537

Let's make it clear: We need more frameworks for evaluating data protection risks in AI systems. As I delve into this topic, more and more new papers and risk assessment approaches appear. One of them is described in the paper titled "Rethinking Data Protection in the (Generative) Artificial Intelligence Era." 👉 My key takeaways: 1️⃣ Begin by identifying the data that should be protected in AI systems. Authors recommend focusing on the following: •  Training Datasets •  Trained Models •  Deployment-integrated Data (e.g., protect your internal system prompts and external knowledge bases like RAG). ❗ I loved this differentiation and risk assessment, as if, for example, an adversary discovers your system prompts, they might try to exploit them. Also, protecting sensitive RAG data is essential. •  User prompts (e.g., besides prompts protection, add transparency and let users know if prompts will be logged or used for training). •  AI-generated Content (e.g., ensure traceability to understand its provenance if used for training, etc.). 2️⃣ Authors also introduce an interesting taxonomy of data protection areas to focus on when dealing with generative AI: •  Level 1: Data Non-usability. Ensures that specified data cannot contribute to model learning or predicting in any way by using strategies that block any unauthorized party from using or even accessing protected data (e.g., encryption, access controls, unlearnable examples, non-transferable learning, etc.) •  Level 2: Data Privacy-preservation. Here, the focus is on how the training can be performed with enhanced privacy techniques (PETs): K-anonymity and L-diversity schemes, differential privacy, homomorphic encryption, federated learning, and split learning. •  Level 3: Data Traceability. This is about the ability to track the origin, history, and influence of data as it is used in AI applications during training and inference. This capability allows stakeholders to audit and verify data usage. This can be categorised into intrusive (e.g., digital watermarking with signatures to datasets, model parameters, or prompts) and non-intrusive methods (e.g., membership inference, model fingerprinting, cryptographic hashing, etc.). •  Level 4: Data Deletability. This is about the capacity to completely remove a specific piece of data and its influence from a trained model (authors recommend exploring unlearning techniques that specifically focus on erasing the influence of the data in the model, rather than the content or model itself). ------------------------------------------------------------------------ 👋 I'm Vadym, an expert in integrating privacy requirements into AI-driven data processing operations. 🔔 Follow me to stay ahead of the latest trends and to receive actionable guidance on the intersection of AI and privacy. ✍ Expect content that is solely authored by me, reflecting my reading and experiences. #AI #privacy #GDPR

💨 Rediscovering Graphs—Through the Lens of Privacy-Preserving Computing. Graphs have always been close to my heart. This fascination began during Prof. S. A. Chaudum’s Graph Theory course at IIT Madras—one of the most transformative courses of my life. Even though I didn’t formally pursue graph theory as my main research area, graphs kept finding their way back into my work—through secure algorithm design or proofs of security. I was especially delighted when my student Bhavish has chosen to work on secure graph computation, reconnecting me with my all-time favourite mathematical structure. This research direction is deeply meaningful, with applications across finance (fraud detection), traffic systems, social networks (influencer discovery), supply chains, and more. In today’s world, graph data is often distributed, sensitive, and siloed across organisations. Yet, analysing such graphs collaboratively can unlock enormous value. Our work explores how Secure Multiparty Computation (MPC) enables exactly this—allowing multiple entities to jointly run graph algorithms like PageRank, BFS, or Connected Components without ever sharing their private data. Our recent results significantly advance privacy-preserving, scalable, and high-performance secure graph processing. Recent Papers Graphiti: Secure Graph Computation Made More Scalable Nishat Koti, Varsha Bhat Kukkala, Arpita Patra, Bhavish Raj Gopal ACM CCS 2024 | https://lnkd.in/gVCmmjSP GraSP: Secure Collaborative Graph Processing Made Scalable Siddharth Kapoor, Nishat Koti, Varsha Bhat Kukkala, Arpita Patra, Bhavish Raj Gopal https://lnkd.in/g9iRgqYH …and more are on the way! ✨ Stay tuned—another exciting thesis will soon emerge from the CrIS Lab! Bhavish Raj Gopal

Working with LLMs or AI chat tools? You’re probably leaking user data! Here’s the privacy hole no one’s talking about. When users interact with AI apps, they often share sensitive information like names, emails, internal identifiers, and even health records. Most apps send this raw data directly to the model. That means PII ends up in logs, audit trails, or third-party APIs. It’s a silent risk sitting in every prompt. Masking data sounds like a fix, but it often breaks the prompt or causes hallucinations. The model can’t reason properly if key context is missing. That’s where GPT Guard comes in. GPTGuard acts as a privacy layer that enables secure use of LLMs without ever exposing sensitive data to public models. Here's how it works: 1. PII Detection and Masking Every prompt is scanned for sensitive information using a mix of regex, heuristics, and AI models. Masking is handled through Protecto’s tokenization API, which replaces sensitive fields with format-preserving placeholders. This ensures nothing identifiable reaches the LLM. 2. Understanding Masked Inputs GPT Guard uses a fine-tuned OpenAI model that understands masked data. It preserves structure and type, so even a placeholder like `<PER>Token123</PER>` retains enough meaning for the LLM to respond naturally. The result: no hallucinations, no broken logic, just accurate answers with privacy intact. 3. Seamless Unmasking Once the LLM generates a reply, GPTGuard unmasks the tokens and returns a complete, readable response. The user never sees the masking — just the final answer with all original context restored. Key features: 🔍 Detects and masks sensitive data like PII, PHI, and internal identifiers from prompts and files 🚫 Prevents raw sensitive data from ever reaching the LLM 🔁 Unmasks the output so users still get a clear, readable response 🚀 Works with OpenAI, Claude, Gemini, Llama, DeepSeek, and other major LLMs 📄 Supports file uploads and secure chat with internal documents via RAG The best part? It works across cloud or on-prem, integrates cleanly with your existing workflows, and doesn't require custom fine-tuning or data pipelines.