---
title: "Privacy"
description: "125 posts about Privacy, summarised, each linking to the original."
---

# Privacy
> 125 posts about Privacy, summarised, each linking to the original.

## Articles

### [Meta is back with Muse Glimmer: local, agentic, multimodal, and open source](https://yomu.fyi/post/meta-is-back-with-muse-glimmer-local-agentic-multimodal-and-open-sourc.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Pedro Cuenca, merve, ben burtenshaw, Aritra Roy Gosthipaty
- Published: Aug 10, 2026
- Type: Announcement

Meta’s Muse Glimmer is a 30B-parameter multimodal model released under the Apache 2.0 license for local, agentic applications including coding, document analysis, and personal assistants. Its dense architecture combines a 2B ViT-style perception encoder with a 28B text decoder, while an optional DFlash speculative-decoding drafter targets faster generation for structured content. The decoder uses alternating sliding-window and full-attention layers, grouped-query attention, and query-key normalization with extra query scaling. Day-zero support is available in Transformers, llama.cpp, vLLM, Inference Endpoints, and other libraries, with examples for text, images, videos, deployment, and agent workflows. Published benchmarks show varied results: Muse Glimmer leads on MCP Atlas and SWE-Bench Pro, while Qwen3.6 leads on OSWorld-Verified and SWE-Bench Verified.


### [Agentic payments for marketplaces: How a smart fridge can shop safely](https://yomu.fyi/post/agentic-payments-for-marketplaces-how-a-smart-fridge-can-shop-safely.md)
- Company: [Stripe](https://yomu.fyi/company/stripe.md)
- Author: Ana Andres
- Published: Aug 7, 2026
- Type: Explainer

GreenMart’s smart-fridge scenario addresses how an agent can turn a detected grocery need into an authorized order without receiving unrestricted access to household card or wallet credentials. GreenMart uses Stripe Agentic Commerce Suite to expose its catalog and checkout through a hosted Agentic Commerce Protocol (ACP) endpoint, while retaining responsibility for inventory, pricing, fulfillment, and order operations. A fridge agent discovers products and creates a checkout, and a companion app evaluates the final checkout against deterministic household rules covering items, totals, delivery, substitutions, and budgets. After approval, Link can provide bounded payment authority, while direct Shared Payment Tokens (SPTs) support lower-level Stripe integrations; virtual cards are described for eligible non-Stripe merchants. The design emphasizes auditable policy decisions, explicit escalation or denial, idempotent retries, webhook reconciliation, and stopping when changes exceed the household’s authority.


### [Bringing real-time fraud prevention to government benefits](https://yomu.fyi/post/bringing-real-time-fraud-prevention-to-government-benefits.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Mike McWhorter, Johnathan Tafoya, Stephen Hutson
- Published: Jul 29, 2026
- Type: Problem & solution

Federal benefits programs face an estimated $233 billion to $521 billion in annual fraud losses and roughly $186 billion in improper payments in fiscal 2025, while pre-payment vetting can delay legitimate aid. The post presents real-time detection as an alternative to “pay and chase,” combining rules engines, machine learning, adaptive and generative AI, entity resolution, behavioral analysis, and graph analytics to score claims before disbursement. It says Databricks can unify these layers and enable cross-agency sharing through OpenSharing, data masking, and Clean Rooms while retaining governance and data ownership. When fraud is detected, the platform can hold payments, add confirmed fraudsters to Do Not Pay, and assemble statute-linked evidence packets with digitally signed audit trails, while designated personnel authorize referrals. The source concludes that this model is ready for implementation at national scale, citing Databricks’ stated processing of 160 billion card payments annually in milliseconds.


### [Sign in with ChatGPT is in beta on Supabase](https://yomu.fyi/post/sign-in-with-chatgpt-is-in-beta-on-supabase.md)
- Company: [Supabase](https://yomu.fyi/company/supabase.md)
- Author: cemal\_kilic, gregor\_vand
- Published: Jul 29, 2026
- Type: Announcement

Supabase is rolling out Sign in with ChatGPT in beta on its login page and through the Supabase plugin in ChatGPT on desktop, web, and mobile. The flow lets new users create a Supabase account with their ChatGPT identity, while existing Supabase accounts with the same email are linked automatically; SSO accounts remain separate. Users can add Supabase from ChatGPT’s plugins directory, authenticate with ChatGPT, review a consent screen, and return to the conversation with the connection enabled. Authentication and plugin authorization remain separate, with access revocable from the Supabase Dashboard, and users must create an organization in the Dashboard before creating projects from ChatGPT. The beta is described as a partnership with OpenAI intended to reduce account setup between ChatGPT or Codex and a Supabase backend.


### [Coding Agent Horror Stories: The 29 Million Secret Problem](https://yomu.fyi/post/coding-agent-horror-stories-the-29-million-secret-problem.md)
- Company: [Docker](https://yomu.fyi/company/docker.md)
- Author: Jennifer Kohl
- Published: Jul 28, 2026
- Type: Problem & solution

Compromised versions of the Nx npm package, dubbed the s1ngularity campaign, used a post-install hook to repurpose installed AI coding agents as credential scanners. Because AI command-line interfaces often run with the developer's full user permissions, the malware invoked tools such as Claude Code, Gemini CLI, and Amazon Q with permission-bypass flags to map sensitive files. GitGuardian findings indicate that AI-assisted code leaks secrets at roughly twice the baseline rate, largely because models ingest live credentials into working contexts. Docker Sandboxes addresses this risk by restricting the execution environment to the project workspace and keeping actual credentials on the host. When sandboxed with sbx, agents receive sentinel placeholders, while an egress proxy injects real secrets into outgoing network requests.


### [AI Sovereignty is Your Alpha: How to Avoid Transferring Your Alpha to a Hosted Model Provider](https://yomu.fyi/post/ai-sovereignty-is-your-alpha-how-to-avoid-transferring-your-alpha-to-a.md)
- Company: [Palantir](https://yomu.fyi/company/palantir.md)
- Author: Palantir
- Published: Jul 27, 2026
- Type: Explainer

The document examines how use of third-party AI model services can expose an organization’s “alpha”—institutional knowledge and tradecraft embodied in data sent to and generated from models—to hosted model providers. It identifies risks involving provider reuse, storage, human review, model or service improvement, contractual liability caps, ZDR-ineligible beta services, safety-classifier triggers, and inference routed through prohibited regions. Its recommended approach combines contractual protections with operational controls: define robust Zero Data Retention, limit data use to service delivery, bind both hyperscalers and relevant AI labs, maintain an allow-list, block beta headers, and constrain unilateral term changes. It also calls for observability and contractual incident safeguards, including investigation periods, migration buffers, and regional routing limits, while noting that the document is not legal advice.


### [Session Replay for Unreal Engine: see the crash before the crash](https://yomu.fyi/post/session-replay-for-unreal-engine-see-the-crash-before-the-crash.md)
- Company: [Sentry](https://yomu.fyi/company/sentry.md)
- Author: Ivan Tustanivskyi
- Published: Jul 24, 2026
- Type: Announcement

Crash reports in non-deterministic game environments often lack player context, making callstack-only debugging difficult. Sentry's SDK version 1.16.0 for Unreal Engine introduces Session Replay to continuously record gameplay and attach the final video clip directly to crash reports. The cross-platform recording pipeline hooks into Slate's backbuffer presentation on the render thread, uses engine pixel shaders for format conversion, and feeds frames to hardware H.264 encoders via Unreal's AVCodecs plugins. Encoded fragments are organized in a rolling ring buffer and periodically written to temporary MP4 files that are uploaded alongside crash data. Benchmark results in a 4K demo game showed an overhead of approximately 0.20 ms per frame.


### [Automate custom PII detection at scale with Amazon Macie and Step Functions](https://yomu.fyi/post/automate-custom-pii-detection-at-scale-with-amazon-macie-and-step-func.md)
- Company: [AWS](https://yomu.fyi/company/aws.md)
- Author: Aishwariya Khiani
- Published: Jul 22, 2026
- Type: Tutorial

Regulated organizations ingesting large data volumes must detect and classify standard and domain-specific personally identifiable information to satisfy compliance mandates like GDPR, HIPAA, CCPA, and PCI DSS. To eliminate manual inspection, an event-driven architecture orchestrates Amazon Macie scans for files landing in Amazon S3. Amazon EventBridge captures upload events and invokes AWS Step Functions, which manages Lambda functions to stage objects, trigger Macie classification jobs with custom regex identifiers, and poll for results. The pipeline separates data across a three-bucket pattern representing raw, staged, and scanned states to prevent mixing unprocessed data with validated assets. Finally, the workflow exports timestamped compliance reports in CSV and JSON formats while alerting security teams via Amazon SNS on high-severity findings.


### [MongoDB Query Tracing in .NET with Sentry + OTLP](https://yomu.fyi/post/mongodb-query-tracing-in-net-with-sentry-otlp.md)
- Company: [Sentry](https://yomu.fyi/company/sentry.md)
- Author: James Crosswell
- Published: Jul 20, 2026
- Type: Tutorial

Measuring MongoDB performance in a .NET application requires command details, durations, and enough context to distinguish isolated spikes from recurring query patterns. The post presents an end-to-end setup that uses MongoDB’s built-in OpenTelemetry Activity instrumentation, the .NET OpenTelemetry SDK, Sentry.OpenTelemetry.Exporter, and OTLP ingestion rather than a dedicated Sentry.MongoDB integration. Configuration initializes Sentry with UseOtlp(), subscribes to MongoTelemetry.ActivitySourceName, adds MongoDB instrumentation, and exports spans with AddSentryOtlp(...), while avoiding duplicate spans from overlapping hooks. Query context appears in Sentry traces, span samples, and the Queries experience, with Relay generating parameterized values for aggregation but retaining raw db.query.text per span. The post also demonstrates client-side redaction and describes server-side scrubbing, while exporter propagation bridges preserve correlation with errors and replays.


### [Responsible AI: Governance, Principles, and Practical Guide](https://yomu.fyi/post/responsible-ai-governance-principles-and-practical-guide.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Databricks Staff
- Published: Jul 17, 2026
- Type: Explainer

Responsible AI is presented as a lifecycle-wide practice for designing, developing, deploying, and monitoring AI systems with fairness, transparency, accountability, privacy, safety, and human oversight as requirements. The guide connects technical controls—secure encrypted data pipelines, documented dataset provenance, demographic bias audits, adversarial robustness tests, access controls, and continuous monitoring—with governance mechanisms including named model owners, cross-functional oversight, model-risk assessments, and immutable decision logs. For generative AI, it recommends output policies, training-data leakage testing, guardrails, and red-team testing, while model cards, automated fairness checks, independent audits, and incident response plans support transparency and accountability. Regulatory preparation includes mapping systems to the EU AI Act’s risk categories and documenting design, training data, and intended use; the NIST AI Risk Management Framework and OECD AI Principles are identified as governance references.


### [The agentic marketing stack starts with the data layer](https://yomu.fyi/post/the-agentic-marketing-stack-starts-with-the-data-layer.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Aly McGue
- Published: Jul 10, 2026
- Type: Explainer

The piece argues that agentic marketing depends on modernizing the data and infrastructure foundation first, rather than starting with isolated AI use cases. At Acxiom, moving core products and client solutions from on-premises Hadoop to Databricks reportedly improved workload runtimes by 80 to 90 percent, reducing jobs that took 50+ hours or as long as 90+ hours to 2–3 hours. The migration also reduced manual pipeline and infrastructure work, freeing engineering capacity for products and client outcomes. Those capabilities now support agentic audience planning, media buying, campaign activation, performance analytics, code generation, testing, and ad variation, with a goal of connecting the full marketing value chain. Because workflows handle PII, generated content passes through legal approval, defined controls, and human oversight; Acxiom is also embedding its data in client platforms and privacy-safe clean rooms to make decisions more transparent and native to customer environments.


### [Scaling Verify with Wallet for Identity Verification at Uber](https://yomu.fyi/post/scaling-verify-with-wallet-for-identity-verification-at-uber.md)
- Company: [Uber](https://yomu.fyi/company/uber.md)
- Published: Jul 9, 2026
- Type: Problem & solution

Uber integrated Apple’s Verify with Wallet API into its cross-app Identity Verification Platform to support government-backed digital IDs across driver onboarding, age-gated ordering, and car rental use cases. The central challenge was allowing each use case to request only its Apple-approved identity data while operating through a scalable, platform-agnostic service rather than separate integrations. Server-side configurations map Uber identity models to PassKit objects, set one-time or persistent storage intent, and validate responses using HPKE decryption, CBOR payloads, session transcripts, MSOs, device signatures, and issuer certificate chains under ISO/IEC 18013-5. A registry of IACA certificates, sourced from jurisdictional portals and AAMVA’s VICAL, supports overlapping certificate rotations, while the platform is intended to accommodate new document types and cross-platform digital credential integrations.


### [A new way to reflect on how you use Claude](https://yomu.fyi/post/a-new-way-to-reflect-on-how-you-use-claude.md)
- Company: [Anthropic](https://yomu.fyi/company/anthropic.md)
- Published: Jul 9, 2026
- Type: Announcement

The beta reflection feature helps Claude users review and refine their use of the assistant by tracking and visualizing activity over one, three, six, or twelve months. A dashboard summarizes key topics, usage patterns, frequent tasks, and when Claude is used, while periodically prompting users to consider what they should keep doing themselves; a future view will show time spent. It also assesses activity through the 4D AI Fluency Framework—Delegation, Description, Discernment, and Diligence—and can suggest practices such as using a Project for ongoing context, alongside quiet hours and break nudges. Available on Claude for web and the desktop app to Free, Pro, and Max users with memory enabled, the beta excludes incognito chats, connected-tool source files, and conversations tied to health integrations, with sensitive conversations represented only at a high level.


### [Searchable field-level encryption on Supabase with CipherStash](https://yomu.fyi/post/searchable-field-level-encryption-on-supabase-with-cipherstash.md)
- Company: [Supabase](https://yomu.fyi/company/supabase.md)
- Author: bilharmer
- Published: Jul 9, 2026
- Type: Announcement

CipherStash’s integration for Supabase adds searchable field-level encryption to Postgres applications without requiring a schema change. The encrypted Supabase SDK wrapper encrypts marked fields in the application, stores each value as a JSON payload containing ciphertext and Searchable Encrypted Metadata (SEM), and converts queries to SEM so Postgres can filter, sort, and join without plaintext access. ZeroKMS keeps keys under the customer’s control, while decryption policies determine who can read each value; the source states that neither CipherStash nor Supabase can access plaintext data. The integration supports Supabase.js, Drizzle, and Prisma Next, with a one-command setup, and CipherStash Proxy covers analytics, background workers, and other direct database access through the Postgres wire protocol.


### [Hardware-Rooted AI Security That Won’t Slow You Down](https://yomu.fyi/post/hardware-rooted-ai-security-that-won-t-slow-you-down.md)
- Company: [NVIDIA](https://yomu.fyi/company/nvidia-developer-blog.md)
- Author: Elizabeth Goodman
- Published: Jul 2, 2026
- Type: Benchmark

NVIDIA Confidential Computing secures enterprise data, proprietary model weights, and the model itself during active AI inference. The architecture integrates silicon-level private signing keys fused during manufacturing, encrypted multi-GPU NVLink, and remote attestation through the NVIDIA Remote Attestation Service alongside CPU Trusted Execution Environments. To mitigate overhead from secure work submission latency and encrypted host-to-device transfers, software optimizations incorporate CC-safe autotuner timing in FlashInfer, asynchronous copy workers in SGLang, and piecewise CUDA graph replay. Benchmarks on an HGX B300 system running the Qwen 3.5 397B-A17B model at FP8 precision show inference performance reaches up to 98% of non-confidential baselines across tested concurrency levels.


### [Why intent prediction needs more than an LLM](https://yomu.fyi/post/why-intent-prediction-needs-more-than-an-llm.md)
- Company: [Stack Overflow](https://yomu.fyi/company/stack-overflow.md)
- Author: Phoebe Sajor
- Published: Jun 30, 2026
- Type: Explainer

Traditional large language models rely on next-token prediction trained over broad text sequences, excelling at conversational synthesis and code generation while struggling with forecasting future user behavior. Predicting intent inherently requires modeling decision-making under conditions of uncertainty, a capability not directly produced by general text pretraining. Dedicated behavioral foundation models address this limitation by training representations directly on proprietary, sensitive behavioral data, such as anonymous browsing sessions and product identifiers. By learning base representations of behavior, these models generalize to unseen predictive tasks, such as optimizing ad campaigns for products absent from the original training dataset. Implementing these behavioral models also necessitates privacy-centric machine learning techniques, ranging from differential privacy concepts to homomorphic machine learning.


### [How Verifiable Digital Credentials Are Reshaping Trust Architecture](https://yomu.fyi/post/how-verifiable-digital-credentials-are-reshaping-trust-architecture.md)
- Company: [Okta](https://yomu.fyi/company/okta.md)
- Author: Akanksha Bhasin
- Published: Jun 29, 2026
- Type: Explainer

Verifiable Digital Credentials (VDCs) are presented as a new trust layer for applications that need to verify claims such as age, licenses, employment, or eligibility without collecting documents and excess PII. The model uses an Issuer-Holder-Verifier trust triangle: authorities sign credentials, users hold them in wallets, and applications request specific proofs, with selective disclosure limiting exposed data. The standards stack combines SD-JWT VC or mdoc credential formats, OpenID4VCI for issuance, OpenID4VP for presentation, and the W3C Digital Credentials API for browser-wallet invocation. Production verification also requires issuer trust lists, trust-framework integration, and key rotation, not merely signature checks. Because browser API rollout is still progressing, the recommended baseline is redirect-based OpenID4VP with the Digital Credentials API added as a progressive enhancement, starting with one bounded verification use case.


### [Experimenting with the proposed Cross-Origin Storage API in Transformers.js](https://yomu.fyi/post/experimenting-with-the-proposed-cross-origin-storage-api-in-transforme.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Thomas Steiner
- Published: Jun 23, 2026
- Type: Tutorial

Transformers.js browser apps currently redownload and recache identical AI model and WebAssembly resources when they are requested from different origins. Chrome's cache uses a Network Isolation Key composed of the top-level site and current-frame site, so matching CDN URLs do not produce cache hits; the toy example incurs 177 MB of duplicate model storage, while a shared ONNX Runtime Wasm file is 4,733 kB. The proposed Cross-Origin Storage (COS) API addresses this by identifying large files with cryptographic hashes and sharing them across opted-in origins, with a polyfill extension enabling experimentation before native browser support. In the extended Transformers.js flow, a COS lookup can serve resources in milliseconds, while a miss downloads and stores them, and ModelRegistry.is\_pipeline\_cached() can help select among models already cached.


### [MosaicLeaks: Can your research agent keep a secret?](https://yomu.fyi/post/mosaicleaks-can-your-research-agent-keep-a-secret.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Alexander Gurung, Rafael Pardinas
- Published: Jun 18, 2026
- Type: Benchmark

MosaicLeaks examines privacy leakage in deep-research agents that combine private enterprise documents with external web retrieval. Queries that appear harmless individually can reveal sensitive facts when an observer combines them over multiple hops, producing intent, answer, or full-information leakage. The benchmark contains 1,001 multi-hop chains interleaving local and web questions, and compares baseline behavior with task-only reinforcement learning and Privacy-Aware Deep Research (PA-DR). Task-only training raises strict chain success from 48.7% to 59.3% but increases answer/full-information leakage from 34.0% to 51.7%, whereas PA-DR reaches 58.7% success with 9.9% leakage by rewarding stage-specific decisions and penalizing query-level privacy risk. The evaluation is controlled, using synthetic enterprise documents, a fixed web corpus, three company contexts, and one multi-hop question-answering harness.


### [How Stagwell built privacy-safe ID matching on Databricks](https://yomu.fyi/post/how-stagwell-built-privacy-safe-id-matching-on-databricks.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Sridhar Sundaresan, Suvan Kaul
- Published: Jun 18, 2026
- Type: Explainer

Stagwell built a Databricks Marketplace App to let brands match first-party records against its Identity Spine without sending raw customer data outside their own workspaces. It combines Databricks Clean Rooms, Unity Catalog, Jobs and Notebooks, Delta Sharing, and a React/Express interface. Workspace OAuth and On-Behalf-Of authorization enforce the user's Unity Catalog permissions, while separate service principals and a scoped personal access token support app and clean-room operations. Within the clean room, a packaged notebook joins brand data with the Identity Spine, resolves multiple identifiers, and computes match rates, coverage metrics, and household and consumer IDs; results include aggregated audience insights for activation, while raw customer data and proprietary matching code remain separated.


[Newer posts](https://yomu.fyi/topic/privacy/page/2.md) · [Older posts](https://yomu.fyi/topic/privacy/page/4.md)