#encryption

This isn't an inevitability: it's a choice. The ubiquity of surveillance in the age of encryption is a policy choice. The reason companies don't encrypt our data so that they can't use it against us is because they don't have to. Congress hasn't updated American consumer privacy law since 1988, when they passed a law that prohibits video store clerks from disclosing our VHS rentals:

https://pluralistic.net/2025/02/20/privacy-first-second-third/#malvertising

Why hasn't Congress updated our privacy rights since Die Hard was in theaters? Because American cops and spies love commercial internet surveillance. Tech companies and data brokers are a source of fine-grained, off-the-books, warrantless surveillance data that the American state is totally dependent on. There is no difference between "commercial surveillance" and "government surveillance" – they are a fused symbiote and neither could survive without the other:

https://pluralistic.net/2021/04/13/public-interest-pharma/#axciom

Governments have hated encryption since the Clinton era, and have been attempting to subvert it since computers came in beige boxes and modems screamed in agony every time you tried to look at the internet:

https://www.eff.org/deeplinks/2015/04/clipper-chips-birthday-looking-back-22-years-key-escrow-failures

It's no mystery why we don't have federal bans on facial recognition – if we did, ICE wouldn't be able to nonconsensually, warrantlessly steal your face and store it for 15 years (at least):

https://www.404media.co/you-cant-refuse-to-be-scanned-by-ices-facial-recognition-app-dhs-document-says/

Why did the EU allow Ireland to facilitate mass surveillance for a decade after the GDPR's passage? Because European authorities also hate encryption and say that it is a "totally erroneous perception that it is everyone's civil liberty to communicate on encrypted messaging services":

https://www.eff.org/deeplinks/2025/09/chat-control-back-menu-eu-it-still-must-be-stopped-0

The internet could be the most privacy-preserving communications medium in history. Instead, it has ushered in an era of nightmarish surveillance. This isn't a technology problem. It's a policy problem. Criminals spy on us online because our governments wanted to spy on us online, so they let corporations spy on us online.

Imagine what the internet would look like today if, in its early regulatory moments, our elected representatives had demanded privacy, rather than trying to ban it. Sure, some corporations would have spied on us anyway, and criminals would have done their best to compromise our privacy, but criminals and rogue firms wouldn't have been able to attract capital to engage in conduct that was likely to give rise to massive fines and criminal prosecutions for violating the privacy laws Congress never bothered to write for us.

Think of it this way: sure, there are e-commerce sites that are just scams, that take your money and never ship you goods. Those sites don't have IPOs, they're not listed on stock exchanges, and they get shut down or blocked. They exist in the shadows, not in the light. Imagine if that was the kind of commercial surveillance industry we'd gotten: marginal, shadowy, illegal, forever on the run. There would still have been some bad privacy invasions, but these would have been crimes, not Harvard Business Review case-studies:

https://www.hbs.edu/faculty/Pages/item.aspx?num=51748

(And before you email me about that one time Paypal closed your account and kept your money or Ebay wouldn't give you a refund, sure, that's right, those things suck, and the companies should face penalties for them, but their business model isn't stealing money from their customers; but Google and Meta and Apple's business model is 100% stealing data from their customers.)

Instead of treating data theft the way we treat monetary theft, we're now increasingly treating monetary theft like data theft. The legislative formalization of cryptocurrency will now allow companies to steal your money with the same blissful lack of consequence as Google faced for stealing your private information:

https://www.citationneeded.news/issue-89/

We're rounding the corner on a decade since the beginning of the fight against Big Tech, and the efforts to cut it down to size. These keep foundering on the political economy of crushing an all-powerful monopolist – namely, that it is all-powerful.

You can't tax Big Tech:

https://www.canada.ca/en/department-finance/news/2025/06/canada-rescinds-digital-services-tax-to-advance-broader-trade-negotiations-with-the-united-states.html

You can't break it up:

https://www.thebignewsletter.com/p/a-judge-lets-google-get-away-with

Donald Trump has made it clear that he'd rather let Putin annex Brussels than allow the EU to fine tech companies:

https://www.cnn.com/2025/09/05/tech/google-eu-antitrust-fine-adtech

Breakups, taxes and fines are all forms of redistribution, which seek to address the harms of monopoly after the monopoly has been formed. The failure to make privacy protections as inviolable as financial protections is a missed opportunity for predistribution. Bans on data collection, mining, and sale would have prevented these monopolies from forming in the first place. Predistribution is far more effective than redistribution:

https://jacobin.com/2025/10/predistribution-welfare-state-inequality-class

It's amazing to realize that the privacy-invading internet has somehow beaten the encrypted internet. It's crazy that the only entity that will promise to encrypt your data beyond the reach of a data broker, an ad-tech giant, or a government is a ransomware criminal, who will also encrypt your data beyond your reach:

https://www.wired.com/story/state-of-ransomware-2024/

It didn't have to be this way. This wasn't a technology failure. It wasn't a commercial failure. It was a policy failure. Since the 1990s, whenever push came to shove, governments decided that they would rather preserve their ability to spy on us than keep us safe from private spying.

If you'd like an essay-formatted version of this post to read or share, here's a link to it on pluralistic.net, my surveillance-free, ad-free, tracker-free blog:

https://pluralistic.net/2025/10/31/losing-the-crypto-wars/#surveillance-monopolism

Image: Cryteria (modified) https://commons.wikimedia.org/wiki/File:HAL9000.svg

1) baseline information content (literal channel)

You’re still just writing English letters in a fancy coat.

Alphabet size ≈ 26 → per-letter info = log₂(26) ≈ 4.7004 bits/char. A word of length n: ~4.7004·n bits (before English redundancy). Real English has ~1.0–1.5 bits/char entropy at scale, so Gallifreyan doesn’t magically compress; it just reformats.

2) where the fun starts: parametric (covert) channels

These are degrees of freedom a standard reader won’t (or isn’t supposed to) use for decoding. You can quantize them to smuggle extra bits.

Start angle of the word-circle (where the first letter sits). If you quantize to r positions (e.g., “clock hours” → r=12), capacity = log₂(r) bits/word.

Per-letter angular jitter (micro-rotation of each letter glyph relative to its nominal slot). If you allow rℓ resolvable bins per letter: log₂(rℓ) bits/letter.

Dot-count variants that the reader ignores (e.g., choose among D options that still decode the same letter): log₂(D) bits/letter.

Stroke weight choices (t thickness levels distinguishable on the medium): log₂(t) bits/letter.

Radial offset of each word inside the sentence ring (q resolvable tracks): log₂(q) bits/word.

Inter-word angular spacing (s bins for the gap size while keeping order): log₂(s) bits/gap (≈ per word, practically).

Below are the example capacities I calculated because I was bored.

Start angle only: r=12 (“clock positions”) → log₂(12) ≈ 3.585 bits/word.

Per-letter covert mix: rℓ=12 bins of micro-angle + D=4 dot options + t=2 thickness levels → per letter options = 12·4·2=96 → log₂(96) ≈ 6.585 bits/letter.

A 5-letter word using that mix → 5·6.585 ≈ 32.9 covert bits plus 3.585 bits from word start angle ≈ 36.5 covert bits per 5-letter word.

Sentence ring with w words and q=8 radial tracks → extra w·log₂(8) = 3w bits.

You can stack these as long as your drawing/printing medium supports the resolution reliably (more on that below).

3) design-space size (how many distinct encodings exist)

For a single word of length n:

Visible text choices (the actual word): 26ⁿ possibilities. Covert geometry choices (example above): per-letter 96 options · per-word start-angle 12 → 12·96ⁿ variants for the same visible word.

Total design-space for content+covert per word: 26ⁿ · 12 · 96ⁿ = 12 · (26·96)ⁿ = 12 · (2496)ⁿ encodings. Nobody’s brute-forcing that by eyeballing a tattoo. (They don’t need to though since they’ll just read the plaintext like a normal substitution and miss the covert bits. And that’s the whole point.)

4) error model & reliable bin counts (how many bins can you actually use)

All covert capacity hinges on distinguishability under noise.

Let σθ be the standard deviation of angular placement noise (human drawing, scanning, camera skew). For robust decoding, target bin width Δθ ≳ 6σθ (rule-of-thumb for low error). Angular bins available on a circle: r ≈ 2π / Δθ.

If σθ ≈ 0.5° on a decent print/scan, Δθ ≈ 3°, r ≈ 360/3 = 120 bins → log₂(120) ≈ 6.91 bits for an angular parameter (per use). Hand-drawn on skin? Maybe σθ ≈ 1.5–2°, so r ≈ 360/12 ≈ 30 bins → log₂(30) ≈ 4.91 bits. Be conservative.

Linear parameters (radial tracks, line thickness) have analogous limits: minimum resolvable step ≈ 6σ of that metric.

5) layout grammar (formal-ish view)

You can sketch a grammar to reason about degrees of freedom like so:

Sentence -> Ring( radius, thickness, startAngle_s )Word[1..w] Punct?

Word -> Circle( radius_w, rTrack, startAngle_w ) Letter[1..n]

Letter -> BaseGlyph(letterID) ⊕ { microAngle, dotVariant, lineVariant, thicknessVariant }

Decoding-visible parameters: letterID, order, word boundaries. Covert parameters: startAngle_s, startAngle_w, rTrack, microAngle, dotVariant, lineVariant, thicknessVariant, inter-word spacing.

Total covert capacity for a sentence with w words and word lengths

Plug in the earlier example (r_word=12, rℓ=12, D=4, t=2, q=8, r_sent=12) to get concrete bit counts.

6) cryptanalysis implications

Breaking the visible text is trivial if the analyst recognizes the system (monoalphabetic substitution; frequency analysis works with enough text).

Missing the covert channel is very likely to happen. Unless an analyst hypothesizes that angles/thickness/spacing are intentional and quantized, they’ll decode the plaintext and stop. That’s covert security, not cryptographic security.

If you want actual security, layer a real cipher underneath like so:

Encrypt plaintext with Vigenère/OTP/AES → map ciphertext letters to Gallifreyan glyphs → use covert channels for MAC/check bits or extra payload. For example, you can allocate 2–3 covert bits/letter to a parity or CRC; decoder can auto-detect tampering from hand-drawn noise.

7) practical encoding recipes

A. “clock-quantized” low-effort covert channel

Word start angle r=12 (bits/word ≈ 3.585). Per-letter dot variant D=4 (bits/letter = 2). Per-letter micro-angle rℓ=8 (bits/letter = 3). Total ≈ 5 bits/letter + 3.6 bits/word. Very robust on paper; tolerable in tattoos if you keep dots and slot angles clean.

B. “dense but fragile”

rℓ=60 (bits/letter ≈ 5.907), D=4 (2 bits), t=2 (1 bit) → ~8.9 bits/letter. Needs print-level precision. For scans/photos, include error-correcting codes (e.g., BCH/RS) across letters.

8) steganographic throughput in images (quick sanity check)

Say you render a 1000×1000 PNG. If your minimum reliable angle step is ~1°:

r≈360 bins → 8.49 bits per angular parameter.

If you use one angular parameter per letter for a 20-letter sentence, ~170 bits hidden, plus start angles/tracks so ~200+ bits total. That’s modest compared to LSB image stego, but here the carrier is the content itself, not the pixel noise so it is much harder to flag by generic detectors.

9) “why it works” (cognitive cheat codes)