Data Encryption Methods

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

🚨 NEW PEER-REVIEWED RESEARCH: PQC Migration Timelines Excited to share my latest paper published in MDPI Computers: "Enterprise Migration to Post-Quantum Cryptography: Timeline Analysis and Strategic Frameworks." The transition to Post-Quantum Cryptography (PQC) represents a watershed moment in the history of our digital civilization. Organizations planning for a 3-5 year "upgrade" will fail. The reality is a 10-15-year systemic transformation. Key Contributions: 📊 Realistic Timeline Estimates by Enterprise Size: Small (≤500 employees): 5-7 years Medium (500-5K): 8-12 years Large (>5K): 12-15+ years ⚠️ Critical Finding: With FTQC expected 2028-2033, large enterprises face a 3-5 year vulnerability window��migration may not complete before quantum computers break RSA/ECC. 🔬 Novel Framework Analysis: Causal dependency mapping (HSM certification, partner coordination as critical paths) "Zombie algorithm" maintenance overhead quantified (20-40%) Zero Trust Architecture implications for PQC 💡 Practical Guidance: Crypto-agility frameworks and phased migration strategies for immediate action. Strategic Recommendations for Leadership: 1. Prioritize by Data Value, Not System Criticality: Invert the traditional triage model. Systems protecting long-lived data (IP, PII, Secrets) must migrate first, regardless of their operational uptime criticality, to mitigate SNDL. 2. Fund the "Invisible" Infrastructure: Budget immediately for the expansion of PKI repositories, bandwidth upgrades, and HSM replacements. These are long-lead items that cannot be rushed. 3. Establish a Crypto-Competency Center: Do not rely solely on generalist security staff. Invest in specialized training or retain dedicated PQC counsel to navigate the mathematical and implementation nuances. The talent shortage will only worsen. 4. Demand Vendor Roadmaps: Contractual language must shift. Procurement should require vendors to provide binding roadmaps for PQC support. "We are working on it" is no longer an acceptable answer for critical supply chain partners. 5. Embrace Hybridity: Accept that the future is hybrid. Design architectures that can support dual-stack cryptography indefinitely, viewing it not as a temporary bridge but as a long-term operational state. 6. Implement Automated Discovery: You cannot migrate what you cannot see. Deploy automated cryptographic discovery tools to continuously map the cryptographic posture of the estate, identifying shadow IT and legacy instances that manual surveys miss. The quantum clock is ticking. Start planning NOW. https://lnkd.in/eHZBD-5Y 📄 DOI: https://lnkd.in/ejA9YpsG #PostQuantumCryptography #Cybersecurity #QuantumComputing #PQC #InfoSec #NIST #CryptoAgility

Earlier this week, I had the privilege of speaking with Dorit Dor (דורית_דור), CTO at Check Point Software. With 30 years at the forefront of cybersecurity, Dorit embodies innovation and expertise in preventing cyberattacks. Our conversation explored groundbreaking topics like Quantum Computing, AI, and Emerging Cyber Threats. Dorit shared that Quantum Computing has the potential to revolutionise industries and solve some of humanity's greatest challenges. However, it also poses a significant risk to traditional encryption methods. Key Takeaways: 1️⃣ Record Now, Decrypt Later: Adversaries are already recording encrypted communications, planning to decrypt them once quantum computers are powerful enough to break RSA and ECC encryption. This is a critical threat for governments, financial institutions, and other organisations handling sensitive data. 2️⃣ Future-Proof Your Encryption: Organisations must prepare for a quantum-powered future by: 📍Reviewing encryption protocols: Identify vulnerabilities in current systems. 📍Adopting post-quantum cryptography: Technologies resilient to quantum attacks, like those integrated into Check Point Software's #VPN solutions. 📍Implementing encryption agility: Stay ahead by mapping out where encryption is used and prioritising high-risk areas. 3️⃣ Quantum Key Distribution (QKD): A cutting-edge solution that replaces traditional encryption keys with those generated through quantum technology, enhancing the resilience of key exchanges against future quantum threats. Navigating these challenges, organisations are facing the daunting task of identifying where encryption is applied and upgrading systems to meet post-quantum standards. The rise of the "dark web" and adversaries storing sensitive data for future decryption makes proactive measures non-negotiable. 🎯Call to Action - if your organisation handles sensitive files: 👉Review and upgrade your encryption strategy. 👉Embrace technologies like post-quantum cryptography and QKD. 👉Leverage tools like those from #CheckPointSoftware to secure your communications. 🎯For more information review the links below: 💡 Check Point Software - https://lnkd.in/e5YS-uFZ 💡 Wikipedia (Dorit Dor / דורית_דור) - https://lnkd.in/eHBD2q9v 💡 World Economic Forum - https://lnkd.in/ehhxEi2T 💡 RSAConference (resource material available under past contribution & Presentations) - https://lnkd.in/eyXNUZjk 💡 Cyber Threat Alliance - https://lnkd.in/ewnTDMJj 💡 Forbes Council - https://lnkd.in/egHKJT74 💡 DLD Conference - https://lnkd.in/eg_3Qsni 💡 Instagram - https://lnkd.in/eKxUHMJv Quantum computing is both a game-changer and a challenge. Let's prepare now to safeguard the future. #quantumcomputing, #AI, #cyberthreats IT Labs - Your Results-Driven Strategic Partner

📌The financial sector has now moved from quantum awareness to quantum execution. Europol , FS-ISAC , and the Quantum Safe Financial Forum (QSFF), together with major financial institutions, published: “Prioritising Post-Quantum Cryptography Migration Activities in Financial Services” ; a practical migration framework designed specifically for financial institutions. What makes this report particularly relevant for #boards, #regulators, and #CISOs? It introduces a structured prioritisation methodology based on two measurable dimensions: 1️⃣ Quantum Risk Score Derived from: • Shelf life of protected data • Exposure • Severity of compromise 2️⃣ Migration Time Score Derived from: • Solution availability • Execution cost and time • External dependencies Migration Priority is determined by combining both scores into a risk–time matrix (see pages 8–10) of the Report below ⬇️ . ♨️ This shifts the conversation from “When will Q-Day happen?” to “Which business use cases require action now, and which require long-term orchestration?” Two examples in the report illustrate this distinction: 🔹 Points of Sale (#PoS) Medium quantum risk but high migration complexity due to hardware lifecycles, ecosystem coordination, and standardisation uncertainty (pages 12–15) . ⛔️Early planning is essential to avoid costly out-of-cycle replacements. 🔹 Public Websites (#TLS_confidentiality) Medium quantum risk but low migration time due to hybrid schemes such as X25519MLKEM768 already supported by major browsers and CDNs (pages 16–19) . ⛔️This is one of the earliest practical deployment opportunities for quantum-safe protection in production environments. Another important contribution of the report is its focus on cryptographic antipatterns (pages 21–24) . Before large-scale PQC migration, institutions can implement no-regret actions: • Automate TLS certificate lifecycle management • Standardise TLS configurations (TLS 1.3 baseline) • Eliminate legacy cipher dependencies • Remove hard-coded credentials • Strengthen key management governance This approach aligns closely with supervisory expectations: #quantum_readiness must integrate into existing risk frameworks, asset lifecycle planning, and vendor coordination. For financial institutions, the message is clear: ❌Quantum safety is not a single migration event. ❌It is a prioritised, staged governance programme that integrates cryptography, procurement, architecture, and regulatory alignment. Full publication: Europol (2026), Prioritising Post-Quantum Cryptography Migration Activities in Financial Services Available via Europol Publications Office: https://lnkd.in/d2bgsVKm #PostQuantumCryptography #PQC #QuantumRisk #FinancialServices #CybersecurityGovernance #DigitalResilience #CryptoAgility #QuantumTransition #FinancialStability

The biggest threat to your data isn’t happening tomorrow. It happened yesterday. If you haven’t heard of HNDL (Harvest Now, Decrypt Later), your long-term data strategy has a massive blind spot. Here is the reality: State actors and cybercriminals are capturing your encrypted data today. They can’t read it yet, so they’re storing it in massive data vaults, waiting for the "Qday"—the moment quantum computers become powerful enough to break current encryption. If your data needs to stay private for 5, 10, or 20 years, it’s already at risk. What’s on the line? ↳ Intellectual Property (IP) and trade secrets. ↳ Government and identity data. ↳ Long-term financial records and contracts. ↳ Sensitive customer health data. How do we solve it? 🛠️ We cannot wait for quantum supremacy to react. The fix starts now: ↳ Inventory: Identify which data has a long shelf-life. ↳ Crypto-Agility: Move toward systems that can swap encryption methods without a total overhaul. ↳ Hybrid PQC: Implement Post-Quantum Cryptography alongside classical methods to ensure traffic captured today remains a mystery tomorrow. The transition to quantum-resistant security is a marathon, not a sprint. Are you tracking HNDL on your current risk register? Let’s discuss in the comments. 👇 P.S. If you want help mapping your exposure or building a PQC migration plan, drop me a message. ♻️ Share this post if it speaks to you, and follow me for more. #QuantumSecurity #PQC

💨 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

EY’s perspective on securing against #quantum #risks emphasizes that quantum #computing is rapidly evolving from a theoretical concern into a material cybersecurity threat that requires immediate strategic action. The core issue lies in the vulnerability of widely used cryptographic algorithms, such as RSA and elliptic curve cryptography, which could be broken by sufficiently advanced quantum computers. This creates a systemic risk to sensitive data, including financial information, intellectual property, and personal records. A central concept highlighted is the “harvest now, decrypt later” threat model, in which adversaries collect encrypted data today with the intention of decrypting it in the future as quantum capabilities mature. This makes quantum risk a present-day problem, particularly for data requiring long-term confidentiality. EY stresses that organizations must adopt a proactive and structured approach to quantum readiness. A foundational step is to conduct a comprehensive cryptographic inventory, identify sensitive #data, and map existing #encryption methods. This enables organizations to assess which systems are most exposed and prioritize remediation efforts. Transitioning to post-quantum cryptography (PQC) is a complex, multi-year transformation that requires careful planning, integration into existing #technology roadmaps, and alignment with emerging standards. Organizations are encouraged to build crypto-agility, allowing them to adapt encryption methods as technologies and standards evolve. EY also highlights the importance of #governance, #compliance, and #workforce readiness. Quantum resilience requires enterprise-wide coordination, including policy development, regulatory alignment, continuous monitoring, and personnel training. EY frames quantum cybersecurity not just as a technical upgrade but as a strategic #transformation initiative. Organizations that act early can strengthen resilience, improve cyber maturity, and gain a competitive advantage, while those that delay risk long-term exposure to data breaches, regulatory challenges, and erosion of #digital #trust.