标签：#Privacy

When a form asks for an image under 100KB, the obvious reaction is to search for an online compressor and upload the file. That works, but it also adds an unnecessary privacy decision: does this image need to leave the device at all? A simpler workflow For ID photos, screenshots, receipts, and other personal images, I prefer tools that do the work locally in the browser. The browser reads the file, resizes or recompresses it, and gives the result back without sending the original to a remote server. My practical process is: Start with the original JPG, PNG, or WebP. Set the required maximum size rather than guessing a quality percentage. Keep the aspect ratio unless the destination specifies exact dimensions. Preview the result at normal size, especially around text and faces. Save the new file under a different name so the original remains untouched. Why target size matters A generic “compress” button may produce a smaller file, but not necessarily one that meets a strict upload limit. A target-size workflow is more useful because it can adjust dimensions and quality together. For many document portals, a visually clean 80–95KB result is safer than a 99.9KB result that may fail after metadata is added. PNG is excellent for flat graphics and screenshots, while JPG is often better for photos. WebP can be efficient, but some older upload forms still accept only JPG or PNG. The destination's rules should decide the output format. The tool I use I built Resize Image around this browser-local approach. It is useful when I need a quick image under a specific size and do not want the original uploaded as part of the resizing process. The link is included for context and disclosure: I am the maker. Local processing does not remove every privacy concern—you should still review the downloaded result and the site where you eventually upload it—but it reduces one unnecessary transfer. The larger lesson is simple: for lightweight image work, the browser is already capable enough

An increasing number of people seem to agree the internet is terrible for children - allegedly addictive, destructive to self-esteem, possibly a portal to predators. Over the past year, several countries have started requiring stringent age verification or outright bans for minors. At the end of June in the US, the House of Representatives passed […]

For the last few months, I’ve been obsessed with a specific problem: the friction between privacy and utility in modern AI tools. Most "private" AI solutions still rely on a local LLM running on your CPU or GPU via a heavy desktop application. They require installation, constant background processes, and often struggle with performance on older hardware. I wanted to see if we could do better. I wanted to see if we could run a capable language model entirely within the browser, using only the device’s hardware acceleration, with zero data leaving the machine. The result is PrivateScribe, a tool I built to handle note summarization, email drafting, and rewriting. But more importantly, it’s an experiment in what’s possible when you treat the browser not just as a display layer, but as a compute engine. The Wedge: WebGPU and True Offline The core constraint that drove this project was simple: nothing leaves the device. In the current landscape, "on-device AI" often means "installed on your device." This is fine for desktop apps, but it creates silos. You can’t easily share a workflow across a Chromebook, a Windows machine, and an iPad without installing three different native applications. By leveraging WebGPU, PrivateScribe runs entirely in the browser. This unlocks a few critical advantages: Zero Installation: Users open a URL and start working. No downloads, no permission dialogs for file system access beyond what’s needed for the session. Hardware Acceleration: WebGPU allows the browser to tap directly into the GPU. This is crucial for inference speed. A small model that runs in your browser can process text significantly faster than a CPU-bound implementation, especially on modern laptops with integrated graphics. True Offline Capability: Because the model weights are loaded locally via WebAssembly and the inference happens on-device, the app works completely offline. If you lose your internet connection in the middle of drafting an email, the AI doesn’t stop. It c

Plus: The Pentagon is training amateurs to become part of its hacker army, a Flock license plate reader error led to cops surrounding a car reviewer, and more.

On July 9, 2026, the European Parliament reauthorized a law that lets tech companies scan your private messages without a warrant. Here is the part that should worry you: a majority of lawmakers voted against it. 314 MEPs voted to kill the law. 276 voted to keep it. The law passed anyway. How? The rejection required an "absolute majority" of 361 votes out of 720 MEPs. Every absent MEP effectively counted as a yes. The vote was scheduled for the last day before summer recess, when many MEPs had already left Strasbourg. The European People's Party, Parliament's largest group, used a rarely invoked urgency procedure (Rule 170) to force the vote onto the floor on July 7. Two days later, the deed was done. What Chat Control 1.0 Actually Does The law is technically called the ePrivacy derogation. It allows tech companies to voluntarily scan private, unencrypted messages and emails for known child sexual abuse material (CSAM), without a warrant or prior suspicion. Platforms affected: Instagram DMs, Discord, Snapchat, Skype, Xbox messages, Gmail, and iCloud. Platforms not affected: WhatsApp and Signal, because they use end-to-end encryption. Parliament did adopt an E2EE exemption amendment with 369 votes, excluding "communications to which end-to-end encryption is, has been or will be applied" from the scanning scope. But here is the catch. Providers of E2EE services were not scanning messages anyway. The exemption preserves the status quo. It does not create new protections. The scanning is limited to "known" CSAM material, meaning previously identified photos and videos. It does not detect new or unknown material. And it remains in effect until 2028, or until a permanent regulation is agreed. The Numbers That Should Make You Doubt This Law The EU Commission's own evaluation report gives Chat Control a very poor assessment: Only 0.00000077% of messages scanned in the EU actually contained illegal material ( heise online ) False positive rates of filter technologies reach u

In the near future, AI -powered surveillance systems will be able to track everything we do in public, and much of what we do in private. And if we do something wrong—shoplift, litter, jaywalk, you name it—the system will notice, retain it, tie it to your official government record, communicate that fact to you, and provide real-time alerts to any relevant authorities… and maybe also to the general public. Think of these systems as automated speed cameras, but on steroids. Only they’ll enforce not just speed limits, but any other rule you can imagine. And you won’t receive a ticket weeks later by mail; you’ll be informed about and fined for your violation immediately...

The dirty secret of online dev tools Every dev tool lives on a different website. jwt.io for JWT decoding. regex101 for regex testing. Some random site for JSON formatting. Another for diff checking. Another for curl → code. Another for SQL formatting. You end up with 10 bookmarks, 10 different UIs, and 10 different servers that just received your most sensitive data — and you never think twice about it. I didn't either. Until I did. What actually happens when you use these tools Let's take jwt.io as an example. Your JWT contains: Your auth algorithm Your user ID Your roles and permissions Your token expiry Sometimes your email, name, org ID When you paste it into jwt.io — it hits their server. It's in their request logs. Maybe forever. The same goes for regex101. Your regex patterns often encode business logic — validation rules, data formats, internal naming conventions. That goes to their database. And every online JSON formatter, diff checker, SQL tool, .env checker you've ever used? Same story. You're not just sharing data. You're sharing the shape of your system. Most of the time nothing bad happens. But "most of the time" is a terrible security posture for a developer who knows better. I got tired of it The more I thought about it, the more it bothered me. I was pasting production JWTs. Real API keys. Actual .env files with database URLs. Into random websites I knew nothing about. So I built DevTab - devtab.in One tab. 110+ dev tools. Zero server calls. Everything runs 100% in your browser via client-side JavaScript and WebAssembly. Open DevTools → Network while using it. Nothing fires. That's not a marketing claim. It's verifiable in 10 seconds. What's inside JSON tools JSON formatter & validator — real-time, error highlighting with line numbers JSON minifier JSON stringify & parse JSON → TypeScript / Pydantic / Go / Zod types Auth & security JWT decoder — header, payload, expiry countdown, issued-at in human time. Zero network requests. .env diff checker —

What Is Data Masking? Data masking is a technique that replaces sensitive information with realistic but fictitious data, preserving the format and structure of the original while removing its identifiable meaning. The goal is to keep data usable for development, testing, analytics, or sharing — without exposing real personally identifiable information (PII). Common masking techniques include: Substitution — Replace a real value with a plausible fake (e.g., Alice Smith → Jane Doe ). Masking (partial obscuring) — Show only a portion of the value (e.g., 4111-1111-1111-1234 → ****-****-****-1234 ). Redaction — Remove the value entirely. Hashing — Replace with a cryptographic hash. Irreversible, but deterministic when salted. Data masking is widely used in non-production environments, analytics pipelines, data marketplaces, and any scenario where real PII is not needed but structural fidelity is. What Is Dynamic Data Masking? Static data masking (SDM) applies transformations to data at rest — you clone a production database, mask it, and ship the masked copy to a lower environment. The masking happens once, and the result is a permanent dataset. Dynamic data masking (DDM) applies transformations on the fly , at query or API time, based on who is asking. The original data stays untouched; the masking rules are applied in the response layer. This means: Different roles see different levels of detail (e.g., support agents see the last 4 digits of a credit card; auditors see the full number). No masked copies to maintain — one source of truth, many views. Masking policies are centralized and enforceable without application changes. Veramask implements a DDM-style model over an API: you send a request with payload and settings, and receive back the transformed result in real time. No data is persisted on the server — each call is independent and stateless. Anonymizing PII with the Veramask API Veramask exposes two endpoints for dynamic PII masking: Endpoint Input Use Case PO

Companies will once again be allowed to scan citizens’ personal texts, emails, and social media messages via the “Chat Control” bill to find child abuse material online.

A few hours after checking into a hotel in New York City, David Streever woke up to a call from the front desk saying someone was looking for him. Streever had just landed on a return trip from Finland, where he'd vacationed with his daughter. Though Streever didn't know it yet, while he'd been away, […]

A government report claims DOGE didn’t access sensitive systems. It also says the agency deleted records that would show if they had.

Meta might be the next company to make an always-on AI wearable. The company is working on prototype "super sensing" always-aware smart glasses that could continuously record audio and snap photos "every few seconds," according to the Financial Times. The wearer could then ask Meta AI about the captured audio and images. However, the images […]

Matomo is an open-source web analytics platform that keeps full ownership of visitor data on infrastructure you control, a privacy-first alternative to Google Analytics. This guide deploys Matomo using Docker Compose with a MariaDB backend, Nginx as the entry point, and Certbot issuing the TLS certificate before the stack goes live. By the end, you'll have Matomo tracking a website with a signed HTTPS certificate at your domain. Prepare Docker $ sudo usermod -aG docker $USER $ newgrp docker Set Up the Project 1. Create the project directory: $ mkdir matomo && cd matomo 2. Open the firewall: $ sudo ufw allow 80/tcp $ sudo ufw allow 443/tcp 3. Create the environment file: $ nano .env MYSQL_ROOT_PASSWORD = your_strong_mysql_root_password MYSQL_PASSWORD = your_strong_mysql_matomo_password Use passwords of at least 16 characters. 4. Create the Nginx config: $ mkdir nginx $ nano nginx/matomo.conf server { listen 80 ; server_name matomo.example.com ; location /.well-known/acme-challenge/ { root /var/www/certbot ; } location / { return 301 https:// $host$request_uri ; } } server { listen 443 ssl http2 ; server_name matomo.example.com ; ssl_certificate /etc/letsencrypt/live/matomo.example.com/fullchain.pem ; ssl_certificate_key /etc/letsencrypt/live/matomo.example.com/privkey.pem ; location / { proxy_pass http://app:80 ; proxy_set_header Host $host ; proxy_set_header X-Real-IP $remote_addr ; proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for ; proxy_set_header X-Forwarded-Proto $scheme ; } } Replace every matomo.example.com occurrence with your domain — it appears in both server blocks and the certificate paths. Deploy with Docker Compose $ nano docker-compose.yaml services : db : image : mariadb:11.4 command : --max-allowed-packet=64MB restart : always volumes : - matomo-db-data:/var/lib/mysql environment : - MYSQL_ROOT_PASSWORD=${MYSQL_ROOT_PASSWORD} - MYSQL_DATABASE=matomo - MYSQL_USER=matomo - MYSQL_PASSWORD=${MYSQL_PASSWORD} networks : - matomo-net app : image

Plausible Analytics is an open-source, self-hosted web analytics tool that tracks traffic without cookies or personal data collection, a privacy-first alternative to Google Analytics. This guide deploys Plausible Community Edition using Docker Compose, fronts it with Nginx, and secures it with a Let's Encrypt certificate. By the end, you'll have Plausible tracking a website's traffic securely at your domain. Configure the Environment 1. Clone the Community Edition repo: $ mkdir ~/plausible $ cd ~/plausible $ git clone https://github.com/plausible/community-edition.git $ cd community-edition 2. Generate a secret key: $ openssl rand -base64 64 | tr -d '\n' 3. Create the environment file: $ nano .env ADMIN_USER_EMAIL = admin@example.com ADMIN_USER_NAME = admin ADMIN_USER_PWD = ADMIN_PASSWORD BASE_URL = https://plausible.example.com SECRET_KEY_BASE = YOUR_SECRET_KEY_BASE DATABASE_URL = postgres://postgres:postgres@plausible_db:5432/plausible_db CLICKHOUSE_DATABASE_URL = http://plausible_events_db:8123/plausible_events_db Fill in your email, a strong admin password, your domain, and the secret key generated above. 4. Expose the app port via a Compose override (auto-merged with compose.yml ): $ nano compose.override.yaml services : plausible : ports : - 127.0.0.1:8000:8000 Deploy with Docker Compose $ docker compose up -d $ docker compose ps Confirm the app, Postgres, and ClickHouse (events DB) containers are all running. Front with Nginx and Let's Encrypt 1. Install Nginx: $ sudo apt update $ sudo apt install nginx -y 2. Create the virtual host: $ sudo nano /etc/nginx/sites-available/plausible.conf server { listen 80 ; listen [::]:80 ; server_name plausible.example.com ; access_log /var/log/nginx/plausible.access.log ; error_log /var/log/nginx/plausible.error.log ; location / { proxy_pass http://127.0.0.1:8000 ; proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for ; proxy_http_version 1.1 ; proxy_set_header Upgrade $http_upgrade ; proxy_set_header Connection "Upgr

Disclosure: I write about projects in the OpenNomos ecosystem, including Swipe Cleaner. The Problem With Photo Cleaners Most photo cleaning apps have a dirty secret: your photos leave your device. They get uploaded to some server for "AI processing," "cloud analysis," or just because the developer didn't think about it. Swipe Cleaner takes the opposite approach. Everything happens on your iPhone. Not a single pixel leaves your device. Let me break down why that matters, and how it actually works under the hood. The Architecture Swipe Cleaner is built on three principles: 1. On-device processing, always. Image analysis, duplicate detection, and similarity matching all run locally using Apple's Core ML and Vision frameworks. No cloud roundtrips, no server costs, no privacy policy loopholes. 2. Tinder-style UX for decisions. You don't manage a grid of thumbnails and checkboxes. You swipe. Right to keep, left to delete. This isn't just a UI gimmick — it's a deliberate choice to reduce decision fatigue. When you have 3,000 photos to clean, you need flow, not friction. 3. Sandboxed storage access. The app requests permission for exactly what it needs. It doesn't ask for your entire photo library if you only want to clean screenshots. This is iOS privacy-by-design done right. Why On-Device Matters Now We're in a weird moment. AI capabilities are exploding, which means the temptation to "send it to the cloud for better results" is stronger than ever. But at the same time, Apple is pushing hard in the opposite direction — Private Cloud Compute, on-device ML, differential privacy. Swipe Cleaner aligns with where the platform is going, not where the industry has been. The Technical Trade-offs Local-first isn't free. Here's what you give up: Model size constraints. You can't run a 70B parameter vision model on an iPhone. The models need to be small, optimized, and ruthlessly efficient. No cross-device sync. Your cleaning decisions stay on one device. No cloud means no sync. For

Anthropic accused of spying on users; engineer says “experiment” is over.

I've long argued that Hollywood has simultaneously set and ruined our expectations for smart glasses. But after binge-watching two seasons of Netflix's A Man on the Inside, this is perhaps the first time I've seen Hollywood, perhaps inadvertently, illustrate the biggest cultural problem with smart glasses as they stand today. In a nutshell, Ted Danson […]

InfoQ has opened enrollment for a five-week AI Security & Privacy Engineering cohort for senior engineers and architects in regulated industries, focused on applying security, privacy, threat modeling, observability, and governance practices to production AI systems. By Artenisa Chatziou

If your memory is hosted, your thoughts are leased. We did not just move our files to the cloud. We moved our working memory. Andy Clark and David Chalmers called it the extended mind in 1998. The thesis was simple. Cognition leaks into the tools we trust. A notebook can be part of your mind if you access it reliably. In 2026, that notebook is a vector database owned by a platform with a legal department. Your extended mind now has terms of service, retention policies, and a compliance team that answers subpoenas faster than you answer email. I am not interested in nostalgia for paper. I am interested in architecture that preserves agency. The fix is not to think less with machines. It is to think locally with machines you control. That is why I built a second brain that lives on a Raspberry Pi 5 with NVMe and a Hailo-8 accelerator, running Retrieval Augmented Generation completely offline. No API keys. No telemetry. No third party that can be compelled to hand over your associative graph. This is the expanded blueprint. More cohesive, more rigorous, and more useful than the usual cloud versus local sermon. The extended mind, now with a landlord The original extended mind argument was about trust and coupling. If you reach for a tool as automatically as you reach for a memory, it counts as cognition. The cloud broke that coupling by inserting a landlord. Your retrieval is fast, but it is also observed, logged, ranked, and retained. Three consequences follow. First, epistemic pollution. When your queries train their models, your future answers are shaped by everyone else’s queries. Your private context gets diluted by the median user. Second, legal exposure. Your prompts, your uploads, your retrieval history, and your embeddings are business records. In many jurisdictions they are discoverable. You cannot plead the fifth for data you gave to a provider. Third, strategic fragility. A policy change, a price hike, a region block, and your cognitive prosthesis goes dark.

I’m building Euro Toolhub , a German-first index of European software, SaaS, cloud and AI alternatives. The idea is simple: many companies, agencies, developers and privacy-conscious users want alternatives to common tools when they care about things like data residency, open source, self-hosting, European jurisdiction or reducing dependency on non-European providers. But most existing lists stop at listing tools. I want Euro Toolhub to go one step further. Each provider profile can include: country and jurisdiction category alternative-to mappings open source status self-hosting availability EU data residency DPA availability certifications where known target audience strengths and limitations a transparent sovereignty score embeddable badges for providers The project starts in German because the first target market is DACH, but the structure is prepared for more languages later. The long-term vision is to build a practical decision platform for digital sovereignty: not just “which European alternatives exist?”, but “which one fits my actual use case?” Current categories include web analytics, cloud and hosting, newsletter tools, CRM, email, password managers, AI APIs, project management, video conferencing and e-signatures. I’m looking for feedback from developers, SaaS founders, privacy people and self-hosting communities: Which European tools should be added? Which categories matter most? What would make a sovereignty score trustworthy? Should the provider dataset be opened through GitHub for corrections and submissions? Project: https://www.euro-toolhub.eu/de Provider submission: https://www.euro-toolhub.eu/de/anbieter-eintragen ``