标签：#privacy

Brave's browser begins testing an email alias feature that lets you keep your main email info private.

The backlash against license plate readers comes amid a wave of police abuses of surveillance cameras.

Meta fixes AI glasses to stop recording any time users cover up the safety light.

California residents have a legal right to access the data that companies collect about them. Actually exercising that right is a burdensome nightmare.

Most "free online tools" have a dirty little secret: the moment you drop a file in, it gets uploaded to someone else's server. Your tax PDF, your ID photo, your client's contract — all sent off to be processed on a machine you'll never see, by a company whose privacy policy you didn't read. For a quick image resize, maybe you don't care. But it adds up. And the wild part is that for most everyday tasks, that upload is completely unnecessary. Modern browsers are powerful enough to do the work right on your own device — no server round-trip, no copy of your file sitting in someone's cloud. Here's how that works, and how to actually use it. Why do so many tools upload your files? Two reasons, mostly. The first is habit: it's easier for developers to send a file to a server, run some code there, and send the result back. The second is business: once your file is on their server, they can log it, analyze it, or use "free" as a funnel toward a paid plan. Watermarks, file-size limits, and "sign up to download" walls all come from this model. The alternative — processing files client-side , meaning inside your browser — has quietly become viable for a huge range of tasks thanks to two technologies: JavaScript (which every browser runs) and WebAssembly (which lets browsers run fast, compiled code at near-native speed). Together they can compress an image, merge a PDF, or transcode data without your file ever leaving the tab. What you can do entirely in your browser You'd be surprised how much works locally now: Images — compress, resize, convert between PNG/JPG/WebP, remove backgrounds, strip metadata. PDFs — merge, split, rotate, compress, and convert to or from images. Text and code — format or minify JSON, count words, change case, generate QR codes, encode/decode Base64. Everyday math — loan, BMI, age, and currency calculators that don't need a server at all. None of these require your data to travel anywhere. The tool loads once, and from then on it's just your CPU doin

Alpharetta, Georgia, cops share data with thousands of Flock users, ranging from federal agencies to a fish and wildlife commission. The reasons why show how vast—and invasive—the network has become.

A recent discovery revealed that AliExpress employs silent audio streams for device fingerprinting, leveraging the Web Audio API. This technique involves analyzing hardware-specific audio processing to distinguish user devices. Privacy-focused browsers have developed countermeasures, highlighting a security gap in current web standards regarding audio context initialization and user privacy. By Olimpiu Pop

After an affair with a fellow police officer ended, a Georgia cop used Flock to track her movements—and those of a man whose vehicle often showed up near hers, internal investigation records show.

Americans have declared war on Flock cameras. They've protested them, damaged them, and destroyed them. This is only the beginning. The spontaneous and decentralized nature of the vigilante protests is also what makes them difficult to suppress. There isn't a uniform method of disruption; people have done everything from unplugging, smashing, stealing, and spray-painting the […]

Open ChatGPT this week and, without any announcement in the chat window, it may already have formed an opinion about how old you are. From 18 August, OpenAI began rolling out “age prediction” on its consumer plans: a system that guesses whether your account belongs to someone under 18 and, if it decides you’re a minor, quietly switches you into a restricted version called ChatGPT for Teens. You are not asked. If the guess lands on “teenager,” the guardrails go up by default. Answer first, because the mechanism matters more than the alarm: the guess is behavioural, and it is admittedly imperfect. By OpenAI’s own account the system reads “general topics you talk about, the times of day you use ChatGPT, how and when your account is used, and how long your account has existed.” And the way to make it stop guessing is not a toggle. It is to prove your age to a third-party verifier called Persona, with a live selfie, a government ID, or both. The choice on offer isn’t whether to be identified. It’s how. None of this arrives from nowhere, and we’ll be fair about why in a moment. But a change that infers a protected characteristic from the content of your conversations, applies real restrictions on the strength of a guess, and offers identity verification as the only exit is worth reading slowly — especially for the adults who will be misclassified, because OpenAI says plainly that some will be. What OpenAI actually switched on The launch has two parts. The visible one is ChatGPT for Teens , announced on 18 August: a version with study-focused features and stronger safety defaults for under-18s. The consequential one is age prediction , the system that decides who gets dropped into it. In OpenAI’s words, “If our system estimates someone is under 18 or they state their age is between 13 and 17, they are automatically placed into ChatGPT for Teens.” It is rolling out globally, with the EU following “in the coming weeks” to fit regional rules. What does the teen experience act

Ring says users can still opt for end-to-end encryption.

Meta settled its latest kids online safety trial with a group of 29 state attorneys general, sparing it from the remainder of a trial that could have cost it hundreds of billions of dollars. Under the terms of the settlement, which resolves claims by a larger group of 47 states and several districts and territories, […]

Ring has a new way to protect your videos. It's developed an encryption method called TAKE, short for Throw Away the Key Encryption, that the Amazon-owned company says will protect your videos without traditional end-to-end encryption. TAKE limits when and why Amazon's cloud can access those videos, while still providing features like smart alerts for […]

The New York Times has a long article ( alt link ) on surveillance systems aimed at babies. They are increasingly using AI. Nanit and its rivals want to own 24/7 health tracking for the sub-four-foot set. And their already astonishing levels of baby data collection are just the beginning. Nanit recently raised $50 million from investors to expand its use of A.I. and use its camera to track speech and language development, motor skills and more, while extending its presence in children’s bedrooms into early adolescence.

You ask a company to delete your data. In a normal system that is a database operation: find the rows that are yours, remove them, done. The mental model of “delete” that privacy law is built on — the GDPR’s right to erasure, most obviously — assumes exactly this: that your data sits somewhere as a discrete record you can locate and destroy. A trained AI model breaks that assumption. Answer first: your data isn’t stored in the model as a record at all. It is dissolved into the model’s parameters — billions of numbers, each nudged a little during training by every example it saw, yours included. There is no row labelled with your name to delete. Removing your influence means changing the numbers, and doing that cleanly is a genuine research problem, not a setting with a toggle. Where your data actually goes when a model “learns” it Training a large model is a process of adjustment. The model makes a prediction, it’s wrong, and an optimiser tweaks its parameters a fraction to make that particular kind of error slightly less likely next time. Repeat across trillions of tokens and those fractional tweaks accumulate into a system that has, in a distributed and lossy way, absorbed patterns from its training data. The key word is distributed . A single document doesn’t live in one identifiable place in the weights; its contribution is smeared across many parameters that also encode a great many other things. Two consequences follow, and they are the whole reason this is hard. First, you cannot point at the part of the model that is “you.” Second, deleting the original document from the training set does nothing to the model that already trained on it — the lesson has been learned and the textbook has been closed. The data is gone; the influence remains. Erasing your data from the training set is like removing a single lump of sugar from a cake that has already been baked. The lump is gone from the recipe. The sweetness is still in the cake. The clean fix that nobody can af

If you run a team on Jira or Confluence, the deal changed on 17 August and the change was opt-out. From that date, by Atlassian’s own account, the content your team writes into its Cloud products — Confluence pages, Jira tickets, the descriptions and comments where the actual work lives — is used by default to train Rovo, Atlassian’s AI assistant. You were not asked to opt in. You were, at best, given a switch and left to find it. Answer first, because the detail matters more than the outrage: there are two settings, and they are not equal. One governs your in-app data — the text itself. The other governs metadata — the derived signals about that text. On the Free, Standard and Premium plans you can turn off the content, but the metadata switch is greyed out; Atlassian’s support page reads, flatly, “You can’t change this setting.” The full off switch, the one that also stops metadata contribution, is available only on Enterprise. Privacy, in other words, is now a plan tier. What actually changed, with the switches named Atlassian’s data-contribution documentation lays out a matrix that is worth reading slowly, because the defaults are doing the heavy lifting. In-app data contribution defaults to on for Free and Standard customers and off for Premium and Enterprise. Every tier can toggle that one. Metadata contribution is a different story: it is on across the board and can only be switched off by Enterprise. So the customer contributing the most by default — content and metadata, both on, no ability to fully stop it — is the one on the cheapest plan who never opened the settings page. The categories are broad. In-app data, per Atlassian’s materials, covers Confluence page titles and body text, Jira work-item titles, descriptions and comments, and custom status and workflow names. Metadata covers the derived layer: readability scores, task classifications (that a ticket is “sales work,” say), story points, sprint end dates, SLA values, and semantic-similarity measure

Envelope encryption con clave por usuario, qué se cifra y qué no, cómo lo puso a prueba una auditoría externa, y el incidente de rendimiento que provocó nuestro propio hardening de seguridad. Montaste tu app con IA rápido: le pides unos datos al usuario, llamas al modelo, guardas el resultado en la base de datos y a producción. Cómodo, sin complicaciones. Hasta que un día miras bien qué estás guardando. En Cuentopia generamos cuentos personalizados para niños. Para personalizar, un padre nos cuenta cómo es su peque: su carácter, qué le da miedo, qué está pasando en casa. El modelo no improvisa sobre la marcha: se apoya en un marco de criterios clínicos y pedagógicos para decidir cómo abordar cada situación, y luego lo reescribe todo en prosa. Visto de golpe, lo que teníamos en la base de datos era el diario emocional de un montón de menores. El RGPD lo trata como categoría especialmente protegida. El sentido común, también. ¿Y si se filtra la base de datos? ¿Y un backup mal guardado? ¿Y un acceso indebido con privilegios de admin? Relájate —bueno, primero asústate un poco; luego relájate—. Te voy a contar cómo pensamos el cifrado en reposo en serio: una arquitectura de tipo envelope encryption , con una clave maestra que no sale nunca de Cloud KMS (Google Cloud) y una clave por usuario que cifra los campos sensibles antes de que toquen la base de datos. Un aviso antes de seguir: te cuento el criterio y las decisiones, no el plano. No vas a encontrar aquí nombres de recursos, rutas de repositorio, ni el detalle exacto que le serviría de receta a alguien con ganas de probar suerte con nuestros datos. Y porque la seguridad honesta se cuenta entera, también te cuento dónde decidimos no llegar y por qué. ✨ Promesa: al terminar vas a entender, con criterio real de producto, cómo una familia sin ser expertos en cripto se planteó cifrar datos de menores — y por qué ciertas decisiones muy concretas no se hacen públicas nunca, ni en el artículo más honesto. El mapa Lo constru

This is a submission for DEV's Summer Bug Smash : Smash Stories. TL;DR. I wrote a scrubbing policy before writing any instrumentation code. I enforced it in a beforeSend hook. I unit tested it. I wrote a comment above the one obviously sensitive line saying exactly what it must never do. Then I intercepted the actual bytes leaving the browser and found a stranger's shoulder injury in them. Every guarantee I had written was about data my code hands to the SDK. None of them were about data the SDK collects on its own. The setup WhyRep is a workout tracker built local-first. Training data is created and read on the device, the tracker works offline with no account, and that is not a marketing line, it is the architecture. It is also the thing people decide to trust or not trust in about four seconds on the landing page. So when I added Sentry, the scrubbing policy came before the code. Written down, in the repo, as a list of things that may never appear in an event: exercise names, weights, reps, RIR, session notes, chat content. Never. On Android I enforced it twice. A beforeSend hook that strips the forbidden fields, and a unit test that constructs an event carrying each one and asserts it comes out stripped. @Test fun `beforeSend strips every field the policy forbids` () { val event = SentryEvent (). apply { setExtra ( "exerciseName" , "Incline Barbell Bench" ) setExtra ( "weightKg" , 82.5 ) setExtra ( "notes" , "left shoulder clicks past parallel" ) } val scrubbed = ScrubbingPolicy . scrub ( event , Hint ()) assertNull ( scrubbed ?. getExtra ( "exerciseName" )) assertNull ( scrubbed ?. getExtra ( "weightKg" )) assertNull ( scrubbed ?. getExtra ( "notes" )) } Green. Good. Then I wired up the landing site's share-link page. It decodes whyrep.com/t#<payload> , where the payload is somebody's entire workout template, base64 in the URL fragment. I was careful there too. On a decode failure it reports a coarse reason tag and never the payload: // NEVER send the payload i

Some fortnights the gripes scatter; this one they converged. Across the forums where paying customers of the big AI tools compare notes, the same complaint surfaced against three different companies in the same window: the monthly plan buys less than it did, and nobody dropped the price to match. One firm’s temporary generosity is about to expire, another’s year-old plan has quietly tightened, and the users caught in the middle are doing the same grim sum and reaching for the same coping strategies. Quotes sourced from: Reddit — specifically the subreddits r/ClaudeCode, r/perplexity_ai and r/Anthropic. Every quote below was opened at its permalink and copied verbatim; each is listed with its handle, subreddit and date in the Sources section. We quote experiences, not verdicts — a forum post is one person’s felt reality, and we have framed it as exactly that. The limit that lapses on the 19th The loudest note this fortnight came from Claude Code users watching a date on the calendar. Anthropic had lifted weekly limits by 50% as a promotion, extended it several times, and set it to expire around 19 August — after which allowances fall back to standard. For anyone who had adjusted their workflow to the higher ceiling, the lapse reads as a cut. A user posting as EnthusiasmMountain10 laid out the worry on 14 August: “I’m seeing more tokens burned on tasks that previously felt straightforward, more meandering, and generally less output per unit of usage. At the same time, the 50% usage reduction coming on Aug 19 makes this particularly concerning.” The complaint was double-barrelled: not only is the ceiling dropping, but the same work seems to cost more against it than it used to. That second half — a sense that quality had slipped — ran through the thread. A commenter posting as Captain_Birb put it bluntly: “quality is down.. 4.6 and 4.8 were sharper. Sonnet 5 is seriously a joke — it gets roasted by Opus every time and admits his faults.” Another, TheSassyPlant , descri

Plus: Apple sends out an “unprecedented” number of spyware warnings, Ukraine hits a Russian ecommerce giant with cyber and drone attacks, and more.