# Security, Privacy and Abuse Prevention Published articles for Security, Privacy and Abuse Prevention. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## A decade of mathematical certainty: Reflections on the Automated Reasoning Group DevFeed: [A decade of mathematical certainty: Reflections on the Automated Reasoning Group]() Original publisher: [Read original article]() Author: Byron Cook Published: 2026-08-11T16:22:19Z Content type: article Language: en Sources: [Amazon Science homepage]() Topics: [Automated reasoning](), [Formal verification](), [Math and Logic](), [systems](), [Amazon Web Services](), [VPC](), [network security](), [Security](), [AWS IAM](), [Amazon S3](), [Cloud](), [amazon]() Tags: [agentic-ai](), [amazon](), [amazon-web-services-aws](), [automated-reasoning](), [aws](), [compliance](), [formal-verification](), [network-security](), [security](), [security-privacy-and-abuse-prevention](), [vpc](), [vulnerabilities]() ### AI overview Amazon's Automated Reasoning Group reflects on a decade of applying mathematical logic, formal verification, and program analysis to AWS security and reliability. The article describes how research projects became production systems, including Tiros for VPC and network analysis and Zelkova for analyzing policies, S3 Block Public Access, and IAM Access Analyzer. ### Source excerpt Ten years after we founded the Automated Reasoning Group, mathematical logic has moved from academic research into production services that secure millions of customer workloads -- demonstrating that systems can be provably correct, not just probably correct. ## New framework for auditing machine unlearning DevFeed: [New framework for auditing machine unlearning]() Original publisher: [Read original article]() Published: 2026-06-10T17:34:55Z Content type: article Language: en Sources: [The latest research from Google]() Topics: [Algorithms & Theory](), [Machine learning](), [datasets](), [Responsibility & Safety](), [Google]() Tags: [ai](), [algorithms-theory](), [compliance](), [datasets](), [models](), [research](), [responsible-ai](), [safety](), [security-privacy-and-abuse-prevention](), [statistical-significance](), [testing](), [training]() ### AI overview Google Research introduces Regularized f-Divergence Kernel Tests, a framework for auditing machine unlearning through black-box statistical comparisons of model outputs. The method is designed to improve sensitivity and flexibility while controlling false positives and reducing false negatives as sample sizes grow. ### Source excerpt Algorithms & Theory ## EC2's formally verified "isolation engine" provides mathematical assurance of virtual-machine isolation DevFeed: [EC2's formally verified "isolation engine" provides mathematical assurance of virtual-machine isolation]() Original publisher: [Read original article]() Author: Dominic Mulligan; Nathan Chong Published: 2026-06-10T15:00:00Z Content type: article Language: en Sources: [Amazon Science homepage]() Topics: [Amazon EC2](), [Rust formal verification]() Tags: [amazon-web-services-aws](), [autocorrode-library](), [automated-reasoning](), [aws-graviton5-security](), [cloud-and-systems](), [confidentiality-integrity-proofs](), [ec2-virtual-machine-security](), [formal-verification](), [formally-verified-hypervisor](), [isabelle-hol-proof-assistant](), [kernel](), [memory-safety-verification](), [nitro-isolation-engine](), [nitro-system](), [provable-security](), [rust](), [rust-formal-verification](), [security](), [security-privacy-and-abuse-prevention]() ### AI overview Amazon describes formally verifying the Nitro Isolation Engine, a separation-kernel component that enforces isolation between EC2 virtual machines. The verification uses Isabelle/HOL and is deployed as an always-on feature for Graviton5 users. ### Source excerpt Splitting the "separation kernel" off from the rest of the Nitro security system and using only a subset of the Rust programming language to code it enabled its formal verification. ## Private analytics via zero-trust aggregation DevFeed: [Private analytics via zero-trust aggregation]() Original publisher: [Read original article]() Published: 2026-05-27T16:56:00Z Content type: article Language: en Sources: [The latest research from Google]() Topics: [Security, Privacy and Abuse Prevention](), [Security](), [Zero Trust](), [Google](), [On-device AI](), [Artificial Intelligence](), [Android]() Tags: [ai](), [android](), [cryptographic](), [on-device-ai](), [privacy](), [security](), [security-privacy-and-abuse-prevention](), [zero-trust]() ### AI overview Google Research introduces a private analytics solution that combines cryptographic secure aggregation with the transparency and attestation properties of trusted execution environments. The design follows a zero-trust principle so Google can obtain only anonymized, aggregated population insights while individual user data remains protected. The article discusses applications of federated analytics and on-device technologies, including Android SafetyCore, Pixel Recorder, and Gboard. ### Source excerpt Security, Privacy and Abuse Prevention ## Safeguarding cryptocurrency by disclosing quantum vulnerabilities responsibly DevFeed: [Safeguarding cryptocurrency by disclosing quantum vulnerabilities responsibly]() Original publisher: [Read original article]() Published: 2026-03-31T02:03:00Z Content type: article Language: en Sources: [The latest research from Google]() Topics: [Cryptocurrency](), [Vulnerabilities](), [Post-quantum cryptography](), [Quantum Computing](), [Cryptography](), [Zero-knowledge proof](), [Google]() Tags: [algorithms-theory](), [awareness](), [coinbase](), [cryptocurrency](), [cryptography](), [ethereum](), [google](), [government](), [migration](), [post-quantum](), [quantum](), [quantum-computing](), [research](), [security-privacy-and-abuse-prevention](), [vulnerabilities](), [zero-knowledge]() ### AI overview Google Research describes how future quantum computers could break the elliptic-curve cryptography protecting cryptocurrency with fewer resources than previously estimated. The article recommends transitioning blockchains to post-quantum cryptography and presents zero-knowledge proofs as a way to disclose vulnerabilities responsibly. ### Source excerpt Algorithms & Theory ## A differentially private framework for gaining insights into AI chatbot use DevFeed: [A differentially private framework for gaining insights into AI chatbot use]() Original publisher: [Read original article]() Published: 2025-12-10T21:59:41Z Content type: article Language: en Sources: [The latest research from Google]() Topics: [Artificial Intelligence](), [AI Chat](), [Large Language Model](), [Google](), [Algorithm](), [pii]() Tags: [ai](), [chatbots](), [clustering](), [code](), [data](), [data-protection](), [generative-ai](), [google](), [insights](), [large-language-model](), [llm](), [pii](), [pipeline](), [research](), [responsible-ai](), [security-privacy-and-abuse-prevention]() ### AI overview Google Research introduces Urania, a framework for generating high-level insights into AI chatbot usage while protecting user conversation privacy. Its pipeline combines differentially private clustering, keyword extraction, and LLM summarization to provide formal, end-to-end differential privacy guarantees. ### Source excerpt Generative AI ## Differentially private machine learning at scale with JAX-Privacy DevFeed: [Differentially private machine learning at scale with JAX-Privacy]() Original publisher: [Read original article]() Published: 2025-11-12T15:32:00Z Content type: article Language: en Sources: [The latest research from Google]() Topics: [Machine Learning & Artificial Intelligence](), [Algorithms, Complexity](), [Deep learning](), [distributed-training](), [data](), [Google](), [High-Performance Computing](), [Programming]() Tags: [algorithms](), [algorithms-theory](), [data](), [deep-learning](), [distributed-training](), [google](), [high-performance-computing](), [libraries](), [machine-learning](), [release](), [research](), [responsible-ai](), [scalability](), [security-privacy-and-abuse-prevention](), [training]() ### AI overview Google announces JAX-Privacy 1.0, a library for differentially private machine learning built on JAX. The release is intended to help researchers and developers implement, audit, and scale private training workflows for deep learning models using large datasets and distributed training. ### Source excerpt Algorithms & Theory ## Toward provably private insights into AI use DevFeed: [Toward provably private insights into AI use]() Original publisher: [Read original article]() Published: 2025-10-30T10:56:00Z Content type: article Language: en Sources: [The latest research from Google]() Topics: [Artificial Intelligence](), [Generative AI](), [Confidential Computing](), [Google](), [trusted-execution-environment](), [data-processing](), [Large language models (LLMs)](), [gemma](), [Open Source]() Tags: [ai](), [confidential-computing](), [data-processing](), [gemma](), [generative-ai](), [google](), [large-language-models-llms](), [mobile-systems](), [on-device](), [open-source](), [research](), [security-privacy-and-abuse-prevention](), [software-systems-engineering]() ### AI overview Google Research introduces provably private insights, a system that combines large language models, differential privacy, and trusted execution environments to analyze aggregate patterns in on-device generative AI use without exposing individual data. ### Source excerpt Generative AI ## A picture's worth a thousand (private) words: Hierarchical generation of coherent synthetic photo albums DevFeed: [A picture's worth a thousand (private) words: Hierarchical generation of coherent synthetic photo albums]() Original publisher: [Read original article]() Published: 2025-10-20T21:54:00Z Content type: article Language: en Sources: [The latest research from Google]() Topics: [Generative AI](), [Synthetic Data Generation](), [synthetic-data](), [Image](), [Google](), [datasets](), [Machine learning]() Tags: [2025](), [ai](), [ai-models](), [data](), [datasets](), [fine-tuning](), [generation](), [generative](), [generative-ai](), [google](), [image](), [images](), [machine-intelligence](), [machine-learning](), [privacy](), [security-privacy-and-abuse-prevention](), [synthetic](), [synthetic-data](), [synthetic-data-generation](), [training]() ### AI overview Google Research introduces a method for generating differentially private synthetic photo albums. The approach translates image data into an intermediate text representation and generates albums hierarchically to preserve thematic coherence and character consistency across multiple photos. It uses differentially private fine-tuning, such as DP-SGD, to produce representative synthetic data without unique details from individual users. ### Source excerpt Generative AI