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NIP-76 Relay Read Permissions #1497

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NIP-76
======

Relay Read Permissions
----------------------

`draft` `optional`

Tags `rp` (read permission) and `prp` (probabilistic read permission) specify which keys are authorized to download an event from the relay.

Events tagged with `rp` or `prp` require AUTH for download.

## Read Permission

The `rp` tag takes a pubkey in lowercase hex as value.

```json
["rp", "<pubkey1>"]
["rp", "<pubkey2>"]
["rp", "<pubkey3>"]
```

When responding to `REQ`s, if an event contains `rp` tags, relays MUST verify that the authenticated user is either the event's author or one of the keys in the `rp` set before delivering it to the client.

## Probabilistic Read Permissions

Probabilistic permissions are implemented using Bloom filters that represent a set of authorized pubkeys. These permissions are expressed as a colon-separated value comprising:
1. the number of bits in the bit array,
2. the number of hash rounds applied, and
3. the bit array encoded in Base64.
4. the salt encoded in Base64.

```json
["prp", "<bits>:<rounds>:<base64>:<salt>"]
```

Bloom filters MUST use `SHA256` functions applied to the concatenation of the key, salt, and index, as demonstrated in the pseudocode below:

```js
class BloomFilter(size: Int, rounds: Int, buffer: ByteArray, salt: ByteArray) {
val bits = BitArray(buffer)

fun bitIndex(value: ByteArray, index: Byte) {
return BigInt(sha256(value || salt || index)) % size
}

fun add(pubkey: HexKey) {
val value = pubkey.hexToByteArray()

for (index in 0 until rounds) {
bits[bitIndex(value, index)] = true
}
}

fun mightContains(pubkey: HexKey): Boolean {
val value = pubkey.hexToByteArray()

for (index in 0 until rounds) {
if (!bits[bitIndex(value, index)]) {
return false
}
}

return true
}

fun encode() {
return size + ":" + rounds + ":" + base64Encode(bits.toByteArray()) + ":" + base64Encode(salt)
}

fun decode(str: String): BloomFilter {
val [sizeStr, roundsStr, bufferB64, saltB64] = str.split(":")
return BloomFilter(sizeStr.toInt(), roundsStr.toInt(), base64Decode(bufferB64), base64Decode(saltB64))
}
}
```

When responding to `REQ`s, if an event contains `prp` tags, relays MUST verify that the authenticated user is either the event's author or matches any of the filters before delivering it to the client.

If both `rp` and `prp` tags are present, the authenticated user MUST either be in the `rp` set or match any `prp` filter.

### Test cases

The filter below has 100 bits and uses 10 rounds of hashing, which should be capable of handling up to 10,000,000 keys without producing any false positives.

```json
["prp", "100:10:AAAkAQANcYQFCQoB:hZkZYqqdxcE="]
```

It includes keys `ca29c211f1c72d5b6622268ff43d2288ea2b2cb5b9aa196ff9f1704fc914b71b` and `460c25e682fda7832b52d1f22d3d22b3176d972f60dcdc3212ed8c92ef85065c`
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@Giszmo Giszmo Sep 15, 2024

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As @jooray mentions, the math must be off here. Also the wording is wrong.

A filter by itself has no false positives ever. It has a false positive rate (FPR).

So what the nip author meant was 10 million npubs not colliding randomly with a bloom filter that has 2 elements, 100 bits and 10 rounds.

With the chosen parameters, the FPR is of the order E-8 - 1 in 26 million, so yes, with 10 million accounts, it's more likely none of them collides with any specific such filter than not but there is no guarantee about this being collision-free.

Suggested change
The filter below has 100 bits and uses 10 rounds of hashing, which should be capable of handling up to 10,000,000 keys without producing any false positives.
```json
["prp", "100:10:AAAkAQANcYQFCQoB:hZkZYqqdxcE="]
```
It includes keys `ca29c211f1c72d5b6622268ff43d2288ea2b2cb5b9aa196ff9f1704fc914b71b` and `460c25e682fda7832b52d1f22d3d22b3176d972f60dcdc3212ed8c92ef85065c`
The filter below has 100 bits and uses 10 rounds of hashing, which would achieve a false positive rate of 1 in 26 million.
```json
["prp", "100:10:AAAkAQANcYQFCQoB:hZkZYqqdxcE="]
```
It includes only the keys `ca29c211f1c72d5b6622268ff43d2288ea2b2cb5b9aa196ff9f1704fc914b71b` and `460c25e682fda7832b52d1f22d3d22b3176d972f60dcdc3212ed8c92ef85065c`

That said, 10 rounds are not a good choice of parameters for 2 elements. I'm not an expert on this and only plaid around with https://hur.st/bloomfilter/ but that tool suggests that with the same filter size but 35 rounds you would get an FPR of 1 in 27 billion.

Edit: As base64 encodes 6bit/char, 100bit is a bad choice, too. You can get 2 more bits for free. This might not sound like much but it brings the FPR down to 1 in 44 billion.

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@vitorpamplona vitorpamplona Sep 15, 2024

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