标签：#Privacy

_Ran into an open-source project called PrivaMesh yesterday and decided to look under the hood since their architecture choice is wild. Basically, it is an iOS chat application that functions without a backend. No central infrastructure, no corporate servers, nothing. The onboarding flow requires absolutely no phone numbers, emails, or personal identifiers. There is no account registry database to hack, which completely eliminates the usual honeypots for data leaks. Instead of routing data through a standard server farm, this thing uses the Solana blockchain as a raw transport layer. Every encrypted payload is wrapped into a transaction and pushed directly to one-time destination addresses. The cryptography stack is actually solid: they combined X3DH handshakes with Double Ratchet for rolling keys and forced fixed-size padding so observers cannot guess the length of your text. The social graph stays fully hidden because the app constantly rotates delivery points and adds decoy traffic to mess with timing analysis. It is a pretty cool practical application of web3 state machines instead of the usual token speculation. Check the repo if you are into decentralized networking._

A new feature added to Comcast's newest routers can detect if there is motion is inside your home without needing traditional motion sensors.

MergePDF is a 100% client-side PDF tool. No backend, no uploads, no sign-up. Here's the architecture, the tricky parts, and why privacy is a feature, not a setting. Every tax season, the same thing happens. Someone in my family asks me to merge a few PDFs. They Google "merge PDF." They click the first result — a slick, friendly-looking site. They upload their tax returns to a server they've never heard of. That bothered me. So I built MergePDF. It merges, splits, rotates, and rearranges PDF pages — and your files never leave your browser. No backend. No sign-up. No ads. No tracking. iLovePDF uploads your tax returns. We don't. This post is about how it works, the parts that were harder than I expected, and why "client-side only" is a design philosophy, not just a technical choice. The pitch in 30 seconds Drop one or more PDFs onto the page. You get a grid of page thumbnails — real, rendered previews of every page. Drag to reorder. Click to select. Rotate, delete, extract a range. Merge everything into one file, or split into single-page PDFs zipped up. Download. Done. Your browser does all of it. There is no server processing documents. There isn't even a server to process documents. The stack It's a Next.js app, but honestly Next.js is just the host here. The interesting parts are all client-side libraries doing real work: No database. No API routes. No auth. No analytics. The only thing in localStorage is your theme preference. Drag-to-reorder that doesn't fight tap-to-select This one took three attempts. The requirement: Tap a thumbnail → select it (emerald ring) Shift-tap → select a range Long-press + drag → reorder Swipe on mobile → scroll the grid (don't drag) The conflict: if the whole card is the drag handle, taps get swallowed. If only a tiny grip icon is the handle, nobody finds it (especially on mobile, where there's no hover). So split produces a ZIP. fflate's zip packages every single-page PDF into one download. Rotations are honored here too — each spl

Proton’s CEO is a champion of encryption for everyone. So why is he going all in on un-encryptable AI?

About 22 miles south of the former mining town of Patagonia, Arizona, down a winding, unpaved mountain road that passes cows grazing on open range, stands a cottonwood tree believed to be at least 200 years old. The "grandmother" tree, as some locals call it, is imperiled and already showing signs of stress, almost as […]

Apple's changing its rules for data collection consent prompts after Germany's Federal Cartel Office accused Apple of giving the prompts a design that favored its own apps. Apple's App Tracking Transparency prompts reportedly cost social media apps nearly $10 billion when they launched with iOS 14.5, making cross-app tracking of users largely opt in. But […]

A delete button is easy to ship. Real deletion is much harder. That gap matters more with AI agents than with normal apps because one user action can scatter data across prompts, traces, memory stores, vector indexes, tool logs, temporary files, model gateways, retry queues, and analytics events. If your product only deletes the visible chat row, the user may be gone from the UI while their data still lives in five backend systems. For AI app builders, this is not just a compliance chore. It is a trust feature. Users will forgive slow answers faster than they forgive a system that says “deleted” but keeps enough context to reconstruct the conversation later. This guide shows how to design an AI agent data deletion pipeline that removes user data for real, proves what happened, and avoids breaking production workflows while doing it. Why AI deletion is different Traditional deletion usually starts with a known record: a user, a project, a file, a message, or a row in a database. AI agents create a messier shape. A single agent run may include: raw user prompt rewritten prompt retrieved documents embeddings cached model input tool arguments tool responses browser snapshots screenshots uploaded files generated artifacts chain-of-thought-like internal notes you should not store memory summaries trace logs billing metadata support debug events queue state approval comments eval replay packets Some of those records are user-visible. Many are not. That is why “delete the chat” is not enough. Agent deletion needs a map of every place where user data can land, plus a workflow that deletes, redacts, or tombstones each location according to its risk and legal retention rules. The failure mode: UI deletion without backend deletion The dangerous pattern looks like this: The user clicks delete. The app removes the conversation from the sidebar. The backend keeps traces, embeddings, prompts, and tool logs for debugging. A restored pointer, support export, analytics query, or vecto

“Memory” is the feature everyone asked for and few thought through. The pitch is lovely: the assistant remembers your preferences, your projects, your writing style , the fact that you are vegetarian and allergic to long emails, so you never have to repeat yourself. In practice, it is one of the most consequential privacy decisions in consumer AI, dressed up as a convenience toggle, and most people flipped it on without reading past the word “remember.” Convenience and surveillance are the same feature The uncomfortable truth is that there is no version of persistent memory that is not also a growing personal record. For the assistant to remember your details, it has to store your details. The thing that makes it feel like it knows you is a file — structured or otherwise — accumulating what you have told it, and inferring more from what you did not. The warmth and the dossier are not two features. They are one feature seen from two angles. A system that remembers everything you tell it is, definitionally, a system that keeps a record of everything you tell it. The friendliness is the interface; the record is the substance. The profile that shapes what you are shown A stored model of who you are does not sit there inertly; it starts to shape the responses you get. That is the entire selling point — a memory-enabled assistant tailors its answers to what it believes about you. But tailoring cuts both ways. Once the system has decided you are a particular sort of person, with particular views and particular tastes, it begins to give you the version of the world it thinks you want, and you lose the ability to know what it would have said to someone it had profiled differently. The personalisation that feels like being understood is also, quietly, a narrowing. We have seen this film before, with recommendation feeds that learned our preferences and then fed them back to us until the preferences hardened into a cage. A memory-driven assistant risks the same dynamic applied

AI agents are starting to remember more than chats. They can watch clicks, typed text, app switches, browser context, files, tool calls, and workflow history. That memory can make an agent feel useful fast, but it can also turn a helpful feature into a quiet privacy incident. If you are building an AI product, do not start with “how much can we capture?” Start with “what is the smallest event stream that still helps the user?” This guide shows a practical privacy filter you can place between raw user activity and agent memory. Why this matters now Recent AI tooling trends point in the same direction: agents are moving from chat boxes into operating systems, browsers, IDEs, customer support tools, analytics dashboards, and workflow automation platforms. The more useful the agent becomes, the more context it wants. That creates a new engineering problem. Traditional app logs record requests and errors. Agent memory records intent, context, and behavior. A raw event can include: What the user clicked What they typed Which customer record was open Which browser page was active Which tool the agent called Which file or message was summarized Which secrets or personal details appeared nearby This is not just observability. It is a privacy boundary. The practical trigger is simple: computer-use agents and workflow agents now need history to resume work, personalize answers, and automate multi-step tasks. But developers, security reviewers, and buyers are asking harder questions about PII, retention, auditability, user consent, and whether agent traces can leak private business data. The common mistake: treating memory like logs Most teams already have logs, traces, analytics events, and support transcripts. So when they add agent memory, they often reuse the same pattern: Capture the event. Save it to storage. Index it for search. Let the agent retrieve it later. That is easy to ship. It is also too broad. Agent memory needs a stricter path because it may be used to genera

At a press conference outside Madison Square Garden, politicians, musicians, and privacy advocates argued for tighter restrictions on how public venues deploy biometric surveillance.

A gift card image is not an ordinary profile photo. It can contain a redeemable code, a PIN, a receipt, an email address, an order number, and location metadata from the camera. A single authorization bug can therefore expose both personal data and something that behaves like a bearer secret. This article designs the upload path as a security boundary. The examples are implementation-neutral TypeScript so the controls can be mapped to your framework, image decoder, object store, and queue. The goal is not “secure file upload” in the abstract. It is a narrower property: Collect only the evidence needed for a decision, keep the original out of normal review paths, and make every retained copy private, attributable, and short-lived. Start with staged disclosure Do not begin by asking for the entire card and receipt. Most first-pass routing decisions need only structured facts: brand and issuing country currency and face value physical card or e-code proof type available whether the redeemable area is still covered Only request an image after those fields show that visual proof is necessary. For the first image, instruct the user to keep the code or PIN covered and exclude unrelated receipt lines. If a later step genuinely needs a live code, collect it through a separate, purpose-built secret field—not as another image in a support chat. That separation changes the failure mode. A bug in the ordinary proof viewer should not automatically reveal a spendable credential. The FTC explains why the distinction matters: someone who has the gift card number and PIN may be able to take the funds even without holding the physical card. Treat those values as secrets, not harmless text printed in a photo. Threat-model the whole path An upload control on the browser is useful feedback, but it is not a trust boundary. Model at least these failures: Threat Example Required control Secret exposure A full PIN appears in a proof image or log Staged disclosure, detection, restricted escal

The Administrative Office of the U.S. Courts told TechCrunch that it will start disclosing how many times judges authorized the use of spyware to wiretap suspected criminals.

Modern encryption is almost always AEAD: authenticated encryption with associated data. AES-GCM and ChaCha20-Poly1305 are the two you meet everywhere, in TLS, in disk encryption, in message formats, in cloud key management. They give you confidentiality plus an authentication tag, and decryption either returns the plaintext or returns an error. The security definition behind that tag is about forgery. An attacker who does not know the key cannot produce a ciphertext that verifies. That definition holds. What it says nothing about is the situation where the attacker does know one or more keys and gets to choose the ciphertext. What a key multi-collision looks like Take AES-GCM. Its authentication tag is computed with GHASH, a polynomial evaluation over a binary field, and the relationship between the ciphertext blocks and the tag is linear in that field. Linearity is convenient for speed, and it is also solvable. Given two keys the attacker controls, K1 and K2, that linearity lets them set up a system of equations and solve for a ciphertext whose tag verifies under both. Decrypting it with K1 yields one plaintext. Decrypting the same bytes with K2 yields a completely different plaintext. Neither decryption throws an error, because from each key's point of view the tag is correct. Both plaintexts can be attacker-chosen and meaningful. The 2019 paper that named this attack demonstrated a file that was a valid image either way, which is where the memorable label came from: the two decryptions showed different pictures, and the second one had a salamander in it that the reporting system never saw. The property that was missing. An AEAD is key committing if a ciphertext can verify under at most one key. AES-GCM, AES-GCM-SIV, and ChaCha20-Poly1305 are not key committing, and were never claimed to be. The property simply was not part of the design goal, and for a long time no widely deployed system depended on it. The system it broke: message franking Here is the problem th

The surveillance company announced it's making a tool called "Audit Assistance" mandatory for all customers, claiming it's already helped catch abuse. But the company has yet to explain how the tool works in detail, raising questions about its effectiveness.

There are over 120,000 of Flock’s automatic license plate reader (ALPR) cameras installed all over the US. Flock’s cameras, and others like them, use AI to identify and track vehicles based on their license plate number, make, model, color, and other info, networked together to track vehicles and people’s movements throughout the day and across […]

I have been an avid privacy advocate for quite some time now. It started with outright rejecting all "Big Brother" tech, and being hyper paranoid with every little detail, willing to sacrifice ease of use, in exchange for added privacy. However, as time went on, I slowly understood what is that I actually consider my "threat model" , and what exactly is my "sweet spot" between privacy and ease-of-use. I'm now back on multiple "Big Brother" tech, with some extra steps, to ensure I get the facilities they provide, while also being wary of my data. However, while I did make this compromise, I was very annoyed I had to make this compromise in the first place. In an ideal world, I would want the tech where everyone actually is, and is the standard for that particular domain, to have privacy features by default, and not be treated as a niche, or a luxury you have to go out of your way to avail. It was this annoyed version of myself, with my strong belief of privacy features and tools being the new norm, I started looking at everything with that lens. And that is how I got concerned about tracking in links and URLs. Try sharing any Instagram post, or YouTube video, by copying its URL, and you will see a bunch of garbage (garbage to you) in the link. Take for example this (fake) link: https://www.instagram.com/p/Cxyz123/?igshid=AbCdEf123456 These links contain something along the lines of utm_* (marketing attribution), or in this case, Ad-Click Identifiers, such as fbclid (Meta), gclid (Google), or igshid (Instagram). These pesky trackers help collect information regarding you, your device, and also help connect you across the internet, mapping your movement as you browse the web. The thing is, while there are good Samaritans who have built tools and websites to get rid of these trackers, and many privacy oriented browsers have introduced a "Copy Clean Link" option while copying the link from the browser, I believe there should be a tool which should not be restricted to a

Surveillance tech company Flock is rolling out updates to address reports of cops across the country abusing its tools to stalk ex-romantic partners and others. CEO Garrett Langley is delivering a mea culpa, and in an interview with The Verge, says he's changed his mind on what responsibility Flock bears for how law enforcement uses […]

Records obtained by WIRED detail hundreds of allegations of Customs and Border Protection workers misusing internal tools to look up romantic interests and track colleagues’ cell phones.

I requested a copy of my data from McDonald’s loyalty program and received an extensive, personalized report that algorithmically predicts my next purchase.

Phone and Bluetooth signals could turn roadside cameras into far richer tracking tools.