brugeman on Nostr: Starting to play with decentralized trust ranking in Spring v0.12. You can estimate, ...
Starting to play with decentralized trust ranking in Spring v0.12.
You can estimate, adjust and publish trust scores for other users - these are estimated from your recent interactions.
Rabble (npub1wmr…g240) has been advocating the TrustNet as a web of trust implementation, useful for spam filtering etc.
The algorithm has two steps - first, each user publishes 'trust assignments' - that's trust scores your can now publish with Spring. These are published as 10629 replaceable events with a list of 'p' tags and a score, typical size will probably be ~100 pubkeys. We provide an estimate based on past interactions, but it can't be precise - you may and should adjust it to match your actual relationships.
The second step is that apps can download trust assignments of users close to your network (contacts, people you like/zap a lot etc) and run a calculation akin to PageRank, but it's not global - it's local to your network. The result will be several thousand pubkeys with non-zero trust ranks - a much wider network of users who could be trusted.
This way the trust ranking is a) based on everyone's actual relationships, because you can adjust the trust scores you're publishing, and b) efficient and can be used by any app - it just needs to download several hundred trust score lists and run the trustnet algo periodically and store results in local cache.
Spring only does step one at the moment. When enough people publish their trust assignments we will add the second step and let you calculate your own trust ranks. Spring will show the trust ranks under profiles, and will use it for spam filtering later. Other apps will probably find other uses for it.
More on TrustNet here:
https://cblgh.org/trustnet/Published at
2023-12-10 07:49:14Event JSON
{
"id": "8b02baf01b4fe572141f38f83545a1c86cd3b6c321b012efe953812419d2d078",
"pubkey": "3356de61b39647931ce8b2140b2bab837e0810c0ef515bbe92de0248040b8bdd",
"created_at": 1702194554,
"kind": 1,
"tags": [
[
"p",
"76c71aae3a491f1d9eec47cba17e229cda4113a0bbb6e6ae1776d7643e29cafa"
]
],
"content": "Starting to play with decentralized trust ranking in Spring v0.12.\n\nYou can estimate, adjust and publish trust scores for other users - these are estimated from your recent interactions.\n\nhttps://void.cat/d/T5HriPK2C8QSd7cGsoJVL6.webp\n\nnostr:npub1wmr34t36fy03m8hvgl96zl3znndyzyaqhwmwdtshwmtkg03fetaqhjg240 has been advocating the TrustNet as a web of trust implementation, useful for spam filtering etc. \n\nThe algorithm has two steps - first, each user publishes 'trust assignments' - that's trust scores your can now publish with Spring. These are published as 10629 replaceable events with a list of 'p' tags and a score, typical size will probably be ~100 pubkeys. We provide an estimate based on past interactions, but it can't be precise - you may and should adjust it to match your actual relationships. \n\nThe second step is that apps can download trust assignments of users close to your network (contacts, people you like/zap a lot etc) and run a calculation akin to PageRank, but it's not global - it's local to your network. The result will be several thousand pubkeys with non-zero trust ranks - a much wider network of users who could be trusted. \n\nThis way the trust ranking is a) based on everyone's actual relationships, because you can adjust the trust scores you're publishing, and b) efficient and can be used by any app - it just needs to download several hundred trust score lists and run the trustnet algo periodically and store results in local cache.\n\nSpring only does step one at the moment. When enough people publish their trust assignments we will add the second step and let you calculate your own trust ranks. Spring will show the trust ranks under profiles, and will use it for spam filtering later. Other apps will probably find other uses for it.\n\nMore on TrustNet here: https://cblgh.org/trustnet/",
"sig": "bd6dbcb32d8e3c01332dfb0ebc06523ecf3d715bde02f1d0bdf5533b88753d0cf8bde5129adc24f738732395f9de71cafc0c30531aaa9a2d17c4e6c80c2349be"
}
