Distinct & compact word lists to represent e.g. fingerprints or other binary data for humans? - cryptography

What word lists / "dictionaries" can you suggest for conveying binary data such as cryptographic fingerprints, hashes etc. ?
Criteria for such a word list are e.g.
Compact, i.e. a long list of rather short words so you need less of those words to transmit data
Distinctive words / simple to distinguish (no homonyms, not even accidentally caused by slight mispronunciation)
Simple to spell

One of the most familiar ones is the PGP word list.

Related

Converting binary data/numbers into a memorable format

What are my options for converting arbitrary binary data into a memorable format?
Are there any widely used standards? What is the most efficient way to do this? I.e. to encode the most data in the easiest-to-remember way?
You can use Bitcoin's BIP-39 standard, whereby numbers are split into 11 bits and each is mapped to a word from a list of 2048 words. These words are chosen whereby similar words are avoided and a word can be unambiguously determined based on the first 4 letters.

What are the characters that count as the same character under collation of UTF8 Unicode? And what VB.net function can be used to merge them?

Also what's the vb.net function that will map all those different characters into their most standard form.
For example, tolower would map A and a to the same character right?
I need the same function for these characters
german
ß === s
Ü === u
Χιοσ == Χίος
Otherwise, sometimes I insert Χιοσ and latter when I insert Χίος mysql complaints that the ID already exist.
So I want to create a unique ID that maps all those strange characters into a more stable one.
For the encoding aspect of the thing, look at String.Normalize. Notice also its overload that specifies a particular normal form to which you want to convert the string, but the default normal form (C) will work just fine for nearly everyone who wants to "map all those different characters into their most standard form".
However, things get more complicated once you move into the database and deal with collations.
Unicode normalization does not ever change the character case. It covers only cases where the characters are basically equivalent - look the same1, mean the same thing. For example,
Χιοσ != Χίος,
The two sigma characters are considered non-equivalent, and the accented iota (\u1F30) is equivalent to a sequence of two characters, the plain iota (\u03B9) and the accent (\u0313).
Your real problem seems to be that you are using Unicode strings as primary keys, which is not the most popular database design practice. Such primary keys take up more space than needed and are bound to change over time (even if the initial version of the application does not plan to support that). Oh, and I forgot their sensitivity to collations. Instead of identifying records by Unicode strings, have the database schema generate meaningless sequential integers for you as you insert the records, and demote the Unicode strings to mere attributes of the records. This way they can be the same or different as you please.
It may still be useful to normalize them before storing for the purpose of searching and safer subsequent processing; but the particular case insensitive collation that you use will no longer restrict you in any way.
1Almost the same in case of compatibility normalization as opposed to canonical normalization.

Unicode question under iOS

I have a SQLite database with a word list. In a table there is a word list that includes the word "você". This word has this representation in unicode "voc\U00ea".
I've found out that the same word can have the following representation with the same visual output:
"voc\U00ea",
"voce\U0302"
When I query my db using the second representation it returns blank. Does anyone know a way for the query work using both representations without duplicating the records in the table?
Thanks,
Miguel
These two forms are known as nfc(normal form composed) and nfd("normal form decomposed"). The letter \U0302 is known as a "combining circumflex", which modifies a preceding letter.
To cope with this situation, do the following:
Pick a normalization. Usually choosing nfc is a good idea. (Although iOS/OS X file system uses nfd.)
Before putting a string into the database, always normalize. In iOS, you can use precomposedStringWithCanonicalMapping or precomosedStringWithCompatibilityMapping. To understand the difference between canonical and compatibility mappings, see this description.
Before performing a query, always normalize the query to the same normal form.

How to convert foreign characters to English characters in SQL Query?

I have to create sql function that converts special Characters, International Characters(French, Chinese...) to english.
Is there any special function in sql, can i get??
Thanks for your help.
If you are after English names for the characters, that is an achievable goal, as they all have published names as part of the Unicode standard.
See for example:
http://www.unicode.org/ucd/
http://www.unicode.org/Public/UNIDATA/
Your task then is to simply turn the list of unicode characters into a table with 100,000 or so rows. Unfortunately the names you get will be things like ARABIC LIGATURE LAM WITH MEEM MEDIAL FORM.
On the other hand, if you want to actually translate the meaning, you need to be looking at machine translation software. Both Microsoft and Google have well-known cloud translation offerings and there are several other well-thought of products too.
I think the short answer is you can't unless you narrow your requirements a lot. It seems you want to take a text sample, A, and convert it into romanized text B.
There are a few problems to tackle:
Languages are typically not romanized on a single character basis. The correct pronunciation of a character is often dependent on the characters and words around it, and can even have special rules for just one word (learning English can be tough because it is filled with these, having borrowed words from many languages without normalizing the spelling).
Even if you code rules for every language you want to support you still have homographs, words that are spelled using exactly the same characters, but that have different pronunciations (and thus romanization) depending on what was meant - for example "sow" meaning a pig, or "sow" (where the w is silent) meaning to plant seeds.
And then you get into the problem of what language you are romanizing: Characters and even words are not unique to one language, but the actual meaning and romanization can vary. The fact that many languages include loan words from those language they share characters with complicates any attempt to automatically determine which language you are trying to romanize.
Given all these difficulties, what it is you actually want to achieve (what problem are you solving)?
You mention French among the languages you want to "convert" into English - yet French (with its accented characters) is already written in the roman alphabet. Even everyday words used in English occasionally make use of accented characters, though these are rare enough that the meaning and pronunciation is understood even if they are omitted (ex. résumé).
Is your problem really that you can't store unicode/extended ASCII? There are numerous ways to correct or work around that.

How do you know when to use varchar and when to use text in sql?

It seems like a very arbitrary decision.
Both can accomplish the same thing in most cases.
By limiting the varchar length seems to me like you're shooting yourself in the foot cause you never know how long of a field you will need.
Is there any specific guideline for choosing VARCHAR or TEXT for your string fields?
I will be using postgresql with the sqlalchemy orm framework for python.
In PostgreSQL there is no technical difference between varchar and text
You can see a varchar(nnn) as a text column with a check constraint that prohibits storing larger values.
So each time you want to have a length constraint, use varchar(nnn).
If you don't want to restrict the length of the data use text
This sentence is wrong:
By limiting the varchar length seems to me like you're shooting yourself in the foot cause you never know how long of a field you will need.
If you are saving, for example, MD5 hashes you do know how large the field is your storing and your storage becomes more efficient. Other examples are:
Usernames (64 max)
Passwords (128 max)
Zip codes
Addresses
Tags
Many more!
In brief:
Variable length fields save space, but because each field can have different length, it makes table operations slower
Fixed length fields make table operations fast, although must be large enough for the maximum expected input, so can use more space
Think of an analogy to arrays and linked lists, where arrays are fixed length fields, and linked lists are like varchars. Which is better, arrays or linked lists? Lucky we have both, because they are both useful in different situations, so too here.
In the most cases you do know what the max length of a string in a field is. In case of a first of lastname you don't need more then 255 characters for example. So by design you choose wich type to use, if you always use text you're wasting resources
Check this article on PostgresOnline, it also links to two other usefull articles.
Most problems with TEXT in PostgreSQL occur when you're using tools, applications and drivers that treat TEXT very different from VARCHAR because other databases behave very different with these two datatypes.
Database designers almost always know how many characters a column needs to hold. US delivery addresses need to hold up to 64 characters. (The US Postal Service publishes addressing guidelines that say so.) US ZIP codes are 5 characters long.
A database designer will look at representative sample data from her clients when she's specifying columns. She'll ask herself, questions like "What's the longest product name?" And when the answer is "70 characters", she won't make the column 3000 characters wide.
VARCHAR has a limit of 8k in SQL Server (I think). Most applications don't require nearly that much storage for a single column.