I didn’t get into this because I suddenly developed a principled stance on privacy. It was operational pain. The kind you can’t route around.
A payment stalled for “review” and then just sat there. No logs, no callback, no actual state transition just 48 hours of nothing followed by a template email from “Risk Management” that might as well have been written by a regex. You’re staring at your own balance like it belongs to someone else. Same story with accounts getting flagged: no reproducibility, no clear invariants, just “decision made” and a dead end.
That’s when it stops feeling like a UX issue and starts looking like a systems problem. These platforms don’t trust you, but more importantly, they don’t expose enough of their own logic to be challenged. There’s no symmetric accountability. You’re inside a black box that can mutate your state without producing verifiable evidence for why.
So I started digging into how most of this “privacy” stack is actually implemented. It’s not privacy in any strict sense. It’s access control layered over centralized data stores. The raw data still exists, usually duplicated across services, piped through analytics, cached in places nobody documents, and logged “temporarily” until that becomes permanent. You’re safe as long as nobody misuses it or until they do. There’s no structural guarantee, just policy and hope.
S.I.N.G takes a different route, but it’s not as clean or magical as people pitch it. It’s basically forcing you into a model where the only thing the system accepts is attestations signed, minimal claims. Not datasets. Not full user records. Just proofs that a condition holds.
That sounds neat until you try to build with it.
Because now every interaction has to be expressed as a verifiable statement with clear semantics. You don’t get to dump a JSON blob and figure it out later. You have to decide upfront what constitutes truth, how it’s proven, and what the minimal disclosure looks like. “Permissionless attestations” sound flexible, but in practice they push complexity to the edges key management, signature verification, revocation logic, replay protection. All the stuff people usually hand wave away with a database write.
The reducer model is where it gets more opinionated. There’s no mutable state you can poke at. State is derived, deterministically, from a set of attestations. If it isn’t attested, it doesn’t exist. If two attestations conflict, you don’t “resolve” it with some ad hoc logic you either have a predefined policy quorum or the state just fails to converge.
That’s great for auditability. You get an immutable audit trail by construction, and you can replay the entire system state from first principles. No hidden branches, no “someone manually fixed it in prod” nonsense.
It’s also a pain.
You lose all the usual escape hatches. No quick patches, no silent overrides, no “just update the row and move on.” Every change has to be expressed as another attestation that passes whatever consensus or policy rules you’ve defined. Latency matters more. Throughput matters more. And if you mess up your schema design early, you don’t get to quietly migrate it without dragging a whole history of attestations along with you hello, state bloat.
From a privacy standpoint, though, it does close a lot of the usual leaks. There isn’t a big underlying dataset to exfiltrate because the system never aggregates raw data in the first place. You can’t “peek” into user information through an internal tool because that information was never stored as a coherent profile. All you have are scoped proofs tied to specific conditions.
That constraint propagates everywhere. APIs become narrower because they can only accept or return attestations. Integrations get harder because you can’t just map fields from one system to another you need compatible proof semantics. Debugging is worse in some ways because you don’t have full visibility; you have to reason from partial, cryptographically verified fragments.
And yeah, growth teams hate this. There’s no data exhaust to hoard, no behavioral firehose to dump into some warehouse and “derive insights later.” You can’t quietly expand your data model because there is no ambient data to expand. Everything has to be explicitly attested, which means explicitly justified.
Developers lose a different set of conveniences. You can’t rely on implicit state. You can’t assume you’ll have full context when handling a request. You have to design for minimal disclosure from the start, which means more upfront modeling, more edge cases, and more failure modes when attestations don’t line up.
But the upside is that a whole category of problems just disappears. There’s no ambiguity about where truth comes from because it’s always tied to a verifiable claim. There’s no “who accessed what” debate because access is structurally limited to what’s proven. You’re not relying on internal policies to prevent abuse; you’re removing the capability in the first place.
It’s not some ideological shift. It’s a different set of trade offs.
You’re swapping flexibility for determinism, convenience for explicitness, and data abundance for enforced minimalism. In return, you get a system where over-collection isn’t just discouraged it’s difficult to even express without breaking the model.
I didn’t go looking for that. I just got tired of systems where the default is to collect everything and explain nothing. This flips that. The default is to reveal nothing unless you can prove why it’s necessary, and the system won’t even accept anything beyond that.
It’s stricter than most teams are comfortable with. Probably for a reason.