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Privacy
16 posts about Privacy. Every summary links to the original.
Airbnb ·
Project Lighthouse — Part 3: Introducing project-lighthouse-anonymize
Airbnb has open-sourced project-lighthouse-anonymize, a Python library powering their privacy-preserving anonymization used to measure user experience disparities. The release is accompanied by technical papers detailing Core Mondrian, an extended partition-based anonymization algorithm, alongside a framework for measuring anonymized data quality. The library enforces k-anonymity and p-sensitive k-anonymity while offering built-in data quality metrics.
Adam BloomstonGithub ·
Your alt text passes automated checks. That doesn’t mean it’s any good.
Automated accessibility checkers reliably flag missing alt text attributes but frequently miss unhelpful descriptions like raw filenames or repetitive labels. To evaluate image description quality without generating excessive false positives, GitHub built an alt text plugin for the GitHub Accessibility Scanner. The tool combines five deterministic, zero-credential rules that evaluate strings and visual layout spacing with an opt-in vision model check for subjective context. Page context including headings and surrounding prose is extracted alongside images to guide the model using structured outputs and explicit anti-nitpick instructions. While deterministic checks catch unwritten text, the model-driven rule serves as an opt-in prompt for human review rather than an absolute verdict.
Taarik AshenafiJunie Can Now Run Entirely on Your Mac – No Credits, No Cloud
JetBrains has introduced Junie Local, a feature that runs the Junie coding agent entirely on local hardware without cloud dependencies, subscriptions, or credit meters. Users execute a single command inside Junie to download a 4-bit quantized Qwen3.6-27B model, requiring an Apple M5 Mac with 64 GB of RAM. The team prioritized prefill throughput over pure generation speed by utilizing 8-bit arithmetic instructions on the M5 Neural Accelerator, which increased prefill throughput by approximately 40%. Performance optimizations also include KV-cache reuse, speculative decoding to double generation speed, and disabling reasoning to preserve performance. In JetBrains' evaluations, the local configuration performed comparably to cloud-based Sonnet 4.5 on everyday tasks while ensuring source code, diffs, and prompts never leave the local environment.
Dmitry SavelevSay it once: introducing Bot Preference Sync
Cloudflare announced Bot Preference Sync, a feature across all customer tiers that automatically reflects dashboard AI bot configurations directly in a site's robots.txt file. Website operators previously faced challenges synchronizing static robots.txt directives with edge enforcement rules, which led some crawlers to ignore stated preferences or attempt bypasses. Under this system, Cloudflare generates and prepends directives for Search, Agent, and Training categories to existing robots.txt files using crawler lists tracked in BotBase. Mixed-use crawlers performing both search indexing and training must meet strict transparency criteria, including honoring opt-outs and providing page-level visibility, to avoid edge blocks when training is disallowed. Additionally, onboarding workflows allow ad-supported publishers to automatically set AI model training to Disallow by default while retaining search visibility.
Jin-Hee LeeMeta ·
How We’re Building Scam Alert on WhatsApp With End-to-End Encryption and Verifiability Guarantees
WhatsApp is introducing Scam Alert, an optional feature designed to detect scam messages from non-contacts while preserving end-to-end encryption guarantees. The system executes text classification entirely on-device using a machine learning model trained on conversational patterns from user reports, ensuring raw message content never leaves the client. To evaluate feature accuracy without compromising privacy, the client transmits minimal pre-aggregated telemetry—consisting only of warning and action counts—over Oblivious HTTP relays to Trusted Execution Environments. These confidential virtual machines securely aggregate metrics and apply differential privacy noise before exposing population-level statistics to servers. System integrity and non-targetability are maintained by requiring clients to attest backend binaries against public transparency ledgers before transmitting any telemetry.
Grab ·
Palana (Part 1): Why Grab built a secure platform for autonomous AI Agents
Autonomous AI agents introduce significant operational and security risks when granted network access, persistent state, and credentials. To address these concerns without impeding developer productivity, Grab created Palana, an in-house Kubernetes-native execution substrate. The platform isolates each agent workload within its own namespace, pairing it with dedicated storage, network policies, and role-based access control. Network egress is funneled through an Envoy and Open Policy Agent proxy layer that audits requests and injects credentials from HashiCorp Vault using placeholder tokens, keeping raw secrets outside the agent runtime. This design allows Grab to securely host hundreds of long-running workflows, remote coding environments, and automation bots.
Kevin LittlejohnGrab ·
Metasense V2: Enhancing, improving and productionisation of LLM powered data governance
Grab scaled Metasense to automate metadata generation, column-level classification, and sensitivity tiering across its entire data lake. Post-rollout analysis revealed that high tag volumes, wide tables, and mixed content—such as nested JSON and customer communications—strained model capacity and led to missed Personally Identifiable Information (PII). To overcome these limitations, the team split the classification process into distinct PII and non-PII tasks, reduced prompt word counts, and partitioned tables with over 150 columns into smaller units. Integrating LangChain and LangSmith modernized the architecture by enabling direct prompt experimentation, custom metric tracking, and version-controlled deployments. The updated pipeline achieved low misclassification rates alongside automated alert thresholds to trigger model improvement protocols if errors increase.
Nick BuhrerGrab ·
LLM-powered data classification for data entities at scale
Grab needed to classify sensitive data at the table and column level across petabytes of database tables and streaming schemas. Manual schema-tiering campaigns had resulted in half of all schemas receiving overly strict Tier 1 access controls, while an initial automated service using regex patterns and third-party machine learning produced high false-positive rates and lacked customizability. To address this, the Caspian data engineering and governance teams enhanced their internal orchestration service, Gemini, by integrating GPT-3.5 via Azure OpenAI. Gemini aggregates classification requests into mini-batches, handles API rate limits, and uses prompt engineering—including few-shot examples, curated tag libraries, and explicit JSON DTO schemas—to reliably tag columns for data owner verification.
Hualin LiuGrab ·
PII masking for privacy-grade machine learning
Data engineers at Grab require real-world streaming data to tune machine learning models, but user personal data cannot be exposed in non-production environments. To solve this, the data streaming team implemented an automated masking pipeline between production and staging Kafka clusters. Developers explicitly tag Personally Identifiable Information (PII) types in Protocol Buffers schemas, which a CI script validates against a keyword list before packaging the schemas into Scala JARs. An in-house Apache Flink application running in production consumes the unmasked streams, applies dynamic and consistent transformations like keyed HMACs or seeded random values, and produces sanitised records to staging. Untagged new fields in schema updates are automatically dropped by the deserialiser until the masking application is redeployed, preventing accidental data leaks.
Fabrice HarbulotGrab ·
How KartaCam powers GrabMaps
Grab's Geo team required an efficient, low-cost way to collect fresh street-level imagery across Southeast Asia, where professional mapping equipment is prohibitively expensive and smartphone crowdsourcing yields inconsistent quality. To address this, Grab built KartaCam, a custom mapping device equipped with a 12MP sensor, dual-band GNSS, 4G LTE, and onboard edge AI. Edge machine learning models evaluate scene suitability, check image quality, filter for map-relevant objects, and blur personal data directly on the device prior to upload. Deploying a four-camera KartaCam 360 array delivers panoramic coverage and point-of-interest data comparable to commercial mapping rigs at roughly one-twentieth the hardware cost.
Shuangquan HouGrab ·
How facial recognition technology keeps you safe
Grab utilizes facial recognition technology across its platform for driver authentication, passenger verification, and digital electronic Know Your Customer (e-KYC) processes. The core pipeline consists of image preprocessing through face detection and alignment, anti-spoofing checks, feature extraction into high-dimensional vector embeddings, and downstream verification or search. To counter spoof attacks like screen replays, synthetic moiré patterns are generated and cropped face patches are used during training and inference to focus on local structures rather than global semantic noise. Face verification challenges involving shallow ID datasets and masked faces are resolved using semi-Siamese training architectures and masked data augmentations.
Kai Feng TeeGrab ·
Biometric authentication - Why do we need it?
Grab addressed the vulnerabilities and costs associated with SMS one-time passwords and PINs by implementing device-level biometric authentication. The architecture pairs device biometric sensors with hardware secure enclaves to protect private keys separately from the main operating system. During enrollment, Grab generates a public-private key pair using SHA512withECDSA, authenticates the user locally, and stores reference identifiers in encrypted device storage. HellfireSDK verifies that the device is not rooted, ensuring raw biometric data never leaves the handset. Early experimental runs indicate an adoption rate exceeding 90% and a login success rate near 90%.
Chad BurgessGrab ·
Protecting Personal Data in Grab's Imagery
Grab's KartaView platform collects geotagged street imagery across over 100 countries, requiring automated obfuscation of faces and licence plates to protect personal privacy. Because off-the-shelf solutions struggled with diverse global environments and equirectangular 360-degree camera formats, Grab built a custom machine learning pipeline. The system projects varied image formats into standardized planar views, applies a YOLOv4 object detection model to locate target regions, and transforms bounding coordinates back to the original imagery for blurring. Training the detector required iterative dataset updates to accommodate edge cases like face masks and mirror reflections, paired with offline view splitting and oversampling of scarce large bounding boxes. Assessments confirmed that obfuscating these regions had minimal negative impact on downstream map feature extraction services.
Adrian PopoviciGrab ·
Grab-Posisi - Southeast Asia’s First Comprehensive GPS Trajectory Dataset
Grab released Grab-Posisi, a GPS trajectory dataset covering Singapore and Jakarta designed to overcome the poor coverage, low sampling rates, and missing contextual metadata common in existing public datasets. Captured in April 2019 from drivers in transit, the dataset comprises 84,000 trajectories, over 80 million GPS pings, and more than 1 million kilometres recorded at a one-second sampling frequency. Each entry in the 2 GB Apache Parquet dataset records geographic coordinates, timestamp, accuracy radius, bearing, speed, mobile operating system, and vehicle mode. To protect privacy, driver personal information is encrypted and trip start and end locations are removed. The data enables applications including automated road network reconstruction, map matching, real-time traffic forecasting, and municipal infrastructure planning.
Zhengmin XuGrab ·
Guiding You Door-to-Door via Our Superapp!
Grab addressed passenger navigation challenges at large Southeast Asian venues such as airports and shopping centers. Satellite signals weaken through concrete and steel, creating GPS inaccuracies that caused the rendezvous distance between passengers and drivers at large venues to exceed twice the average. While introducing Entrances previously mapped over 120,000 green dots to lower rendezvous distances, passengers still struggled to locate specific pickup spots indoors. The company launched Venues, an in-app feature delivering turn-by-turn text and photo directions to designated pickup points. To support this system, operations teams surveyed sites with cameras and scanners to capture landmarks, after which in-house teams masked faces and vehicle license plates.
Neeraj MishraGrab ·
Come and #hackallthethings at Grab
Grab has officially launched a public bug bounty program in partnership with HackerOne to strengthen the security of its platform. This rollout follows a private bounty initiative operated over the previous year, during which the organization worked with over 350 security researchers and resolved nearly 200 awarded bug reports. The new public program invites external researchers to scrutinize Grab's code for critical flaws, including remote code execution, SQL injections, and exportable cross-site scripting vulnerabilities. To support ethical and responsible disclosure, Grab offers payouts reaching up to $10,000 per valid vulnerability report based on severity and impact.
Grab Engineering