In order for privacy preserving technology to see widespread adoption, it must be accessible.
One of the key problems in making this technology accessible is the question of how to handle identity. In a centralised system, some authority can be in charge of which identifiers are associated with which identities. For example, Google runs Gmail, and keep track of which user owns what username. To create an account you ask them for one, such as
email@example.com. They will look up their list of previously registered usernames, and tell you that someone else already has it. Finally, you find an unallocated username
firstname.lastname@example.org, which Gmail grants you. From that point forward, if someone wants to contact you they need your username, then send you their message via Google.
In decentralised systems however this poses some challenges. A global table of registered usernames is a complex engineering challenge.
- How is it stored and shared?
- How does one register new names?
- What happens if two people register names at almost the same time?
- How do identifiers correspond to identities
A sensible alternative is to stick with public-private key pairs, which serve the dual role of acting as both identity and encryption methods.
Messaging systems such as Ricochet and Cwtch purport to be decentralised privacy preserving communication protocols, which handle identity by giving users a pseudorandom name associated with their public key. However, these identifiers are not easy for humans to read or remember for example
This document looks at some ways in which these addresses could be made more usable, by converting our address into a base-2048 encoded string. Taking a cue from BIP-0039, digits in base-2048 are represented by words. For a reminder of what numerical bases are, refer to this helpful post. We choose the words from an arbitrary list, in alphabetical order.
For example, the base-2048 representation of the decimal digit
ability. Below is a table with further examples:
|`100000000`||`actual stuff cactus`|
The full list of words used in this example can be seen in english.txt.
It is worth noting that BIP-0039 used the words as a source of human readable entropy for generating private keys, dubbed a “seed phrase”). One can use an arbitrary word list, as long as one knows what encoding one is using. Thus there are word-lists for multiple languages. However, in our context, since identifiers are supposed to be shared, having parties using different word list creates a potential for error. But settling on using one list of English words might be viewed as anglocentrism. This issue is left open for discussion.
Conversion of Addresses
Let us try converting a Ricochet addresses in our base-2048 format. An example from the Ricochet website is
ricochet:rs7ce36jsj24ogfw. We assume that it is encoded in base-36 (decimal digits + lower-case ASCII letters), and take the 16 characters after the colon.
In the Python code below, we have two functions,
base_n_to_dec()takes a base-N encoded number, N itself, and a dictionary that maps base-N digits to decimal numbers. It returns the base-N encoded number and returns it encoded in base-10.
dec_to_base_n()is the inverse of this operation
This is a proof of concept, so try to ignore implementation peculiarities and inefficiencies.
def base_n_to_dec(base_n_number, base, baseN_to_dec_dict): ret = 0 for i, c in enumerate(base_n_number[::-1]): ret += (base ** i) * baseN_to_dec_dict[c] return ret def dec_to_base_n(number, base, dec_to_baseN_dict): if number == 0: return  digits =  while number: digits.append(dec_to_baseN_dict[int(number % base)]) number //= base return digits[::-1] Python 3.6.4 |Anaconda, Inc.| (default, Jan 16 2018, 18:10:19) [GCC 7.2.0] on linux Type "help", "copyright", "credits" or "license" for more information. python.el: native completion setup loaded
Now let’s get our mapping from base36 to decimal:
import string base36 = string.digits + string.ascii_lowercase base36_to_dec_dict = dict((c, i) for i, c in enumerate(base36)) dec_to_base36_dict = dict((i, c) for i, c in enumerate(base36)) base36_address = "rs7ce36jsj24ogfw" base10_address = base_n_to_dec(base36_address, 36, base36_to_dec_dict) # assert that the operation is reversible (it is) reverse_verify = dec_to_base_n(base10_address, 36, dec_to_base36_dict) reverse_verify = ''.join(reverse_verify) assert(base36_address == reverse_verify)
With that done, now we can convert to our base2048 encoding. We do this in the same way we created our base36 encoding, but using words from our file
with open("english.txt") as f: english_words_list = f.read().splitlines() base2048 = english_words_list base2048_to_dec_dict = dict((c, i) for i, c in enumerate(base2048)) dec_to_base2048_dict = dict((i, c) for i, c in enumerate(base2048))
Finally let’s convert our Ricochet address to base2048
base2048_address = dec_to_base_n(base10_address, 2048, dec_to_base2048_dict) print(base10_address) print(base2048_address) print(len(base2048_address)) # returns ['agree', 'pipe', 'dry', 'song', 'piece', 'bind', 'better', 'pole'] # len = 8 # assert that the operation is reversible (it is) reverse_verify = base_n_to_dec(base2048_address, 2048, base2048_to_dec_dict) reverse_verify = dec_to_base_n(reverse_verify, 36, dec_to_base36_dict) reverse_verify = ''.join(reverse_verify) assert(base36_address == reverse_verify) 6142195001475658028508476 ['agree', 'pipe', 'dry', 'song', 'piece', 'bind', 'better', 'pole'] 8
Okay then, what do we have here now? An 8 word long phrase (
[agree, pipe, dry, song, piece, bind, better, pole]), replacing a 16 character long string. Is this an improvement from a usability standpoint? We cannot say authoritatively without user surveys, but we believe not.
Things get worse when you use an onion-v3 address, which is the format likely to be used by cwtch. A 56 character long base-36 string (
vww6ybal4bd7szmgncyruucpgfkqahzddi37ktceo3ah7ngmcopnpyyd), which we believe is the typical length of an onion-v3 address, is represented by 27 words. This does not seem workable at all.
Longer word list
What if we used a longer word list?
Here, we use the same method as before, but with a word list wiki-100k.txt of length around 100k (scraped from an online dictionary, and a tad untidy).
with open("wiki-100k.txt") as f: english_words_100k_list = f.read().splitlines() base100K = english_words_100k_list dec_to_base100K_dict = dict((i, c) for i, c in enumerate(base100K)) base100K_to_dec_dict = dict((c, i) for i, c in enumerate(base100K)) base100k_address = dec_to_base_n(base10_address, len(base100K), dec_to_base100K_dict) # returns ['meanwhile', 'lining', 'vigor', 'Durham', 'legen']
For the Ricochet address format, this returns a more compact 5 word long string. The onion-v3 address is around 18 words long, which is still beyond reasonable bounds. A key weakness in this approach however is that the word list is around 800KB in size. Though not unworkable, it is a poor allocation of resources to use so much storage, and would raise the barrier of entry for using such a system.
An alternative to using base36 is base56, a format popularised by Bitcoin addresses. In this format, easily confused characters such as capital oh (O) and numeric zero (0) are removed from the symbol set.
base56 = "123456789ABCDEFGHJKLMNPQRSTUVWXYZabcdefghijkmnopqrstuvwxyz" dec_to_base56_dict = dict((i, c) for i, c in enumerate(base56)) base56_to_dec_dict = dict((c, i) for i, c in enumerate(base56)) base56_address = dec_to_base_n(base10_address, 56, dec_to_base56_dict) print(''.join(base56_address)) print(len(base56_address)) 34JXNQZJXJsXri5 15
In the encoding, our Ricochet format address is now 15 characters, down from 16. Our onion-v3 address is 50 characters, down from 56. It is clear that the length reduction is not worth the user-side complexity of including capital letters.
The problem of having human-usable unique identifiers in a decentralised system is an open question, and this work attempts to explore one of the possible. It seems that for most real-world applications, having long addresses represented by human readable word strings is not practical, since these addresses tend to be too long.
Using some sort of decentralised DNS system such as NameCoin or Blockstack is also a possibility, but creates a bit-trail that some users might be unwilling to make.
You can find the word lists we used on our Github repo for this exploration.
After sharing this post with the cwtch community, I was directed to a previous exploration of the idea from SJL. Some of the conclusions were similar, however the work featured an interesting additional component.
This involved using a different word list for each word in a mnemonic, i.e. the first is chosen from an adjective list, the next to a noun, then verb, etc. This would form something like a sentence, as can be seen in SJL’s examples:
the quick fire open the usual dais the fluid ball line the true bag the close seal pay the black coil the gay eft burn the angry fart the warm bank call the live jest
Again, since words lists might not be feasible for representing long addresses, the point is moot. However, with further research on real-world usability, it might be an interesting system to implement in situations where fewer bits are needed.