rdfjs-c14n

RDF Canonicalization in TypeScript

This is an implementation of the RDF Dataset Canonicalization algorithm, also referred to as RDFC-1.0. (The algorithm is being specified by the W3C RDF Dataset Canonicalization and Hash Working Group.)

The specification is not yet final. This implementations aims at reflecting exactly the specification, which means it may evolve alongside the specification even if changes are editorial only.

Requirements

The implementation depends on the interfaces defined by the RDF/JS Data model specification for RDF terms, named and blank nodes, or quads. It also depends on an instance of an RDF Data Factory, specified by the aforementioned specification. For TypeScript, the necessary type specifications are available through the @rdfjs/types package; an implementation of the RDF Data Factory is provided by, for example, the n3 package (but there are others), which also provides a Turtle/TriG parser and serializer to test the library.

By default (i.e., if not explicitly specified) the Data Factory of the n3 package is used.

An input RDF Dataset may be represented by:

• A Set of Quad instances; or
• An Array of Quad instances; or
• A string representing an N-Quads document.

The canonicalization process can be invoked by

• the canonicalize method, that returns an N-Quads document containing the (sorted) quads of the dataset, and using the canonical blank node ids

• the canonicalizeDetailed method, that returns an Object of the form:

  • canonicalized_dataset: a Set or Array of Quad instances, using the canonical blank node ids
  • canonical_form: an N-Quads document containing the (sorted) quads of the dataset, using the canonical blank node ids
  • issued_identifier_map: a Map object, mapping the original blank node ids (as used in the input) to their canonical equivalents
  • bnode_identifier_map: Map object, mapping a blank node to its (canonical) blank node id

• A Set or an Array of Quad instances, if the input was a Set or an Array, respectively;

• A Set of Quad instances if the input was an N-Quads document.

The separate testing folder includes a tiny application that runs the official specification tests, and can be used as an example for the additional packages that are required.

Installation

The usual npm installation can be used:

npm rdfjs-c14n

The package has been written in TypeScript but is distributed in JavaScript; the type definition (i.e., index.d.ts) is included in the distribution.

Usage

There is a more detailed documentation of the classes and types on github. The basic usage may be as follows:

import * as n3  from 'n3';
import * as rdf from 'rdf-js';
// The definition of "Quads" is:
// export type Quads = rdf.Quad[] | Set<rdf.Quad>; 
import {RDFC10, Quads } from 'rdf-c14n';

main() {
    // Any implementation of the data factory will do in the call below.
    // By default, the Data Factory of the `n3` package (i.e., the argument in the call
    // below is not strictly necessary).
    // Optionally, an instance of a Dataset Core Factory may be added as a second argument.
    const rdfc10 = new RDFC10(n3.DataFactory);  

    const input: Quads = createYourQuads();

    // "normalized" is a dataset of quads with "canonical" blank node labels
    // per the specification. 
    const normalized: Quads = rdfc10.canonicalizeDetailed(input).canonicalized_dataset;

    // If you care only of the N-Quads results only, you can make it simpler
    const normalized_N_Quads: string = rdfc10.canonicalizeDetailed(input).canonical_form;

    // Or even simpler, using a shortcut:
    const normalized_N_Quads_bis: string = rdfc10.canonicalize(input);

    // "hash" is the hash value of the canonical dataset, per specification
    const hash: string = rdfc10.hash(normalized);
}

Alternatively, the canonicalization can rely on N-Quads documents only, with all other details hidden:

import * as n3  from 'n3';
import * as rdf from 'rdf-js';
// The definition of "Quads" is:
// export type Quads = rdf.Quad[] | Set<rdf.Quad>; 
import {RDFC10, Quads, quadsToNquads } from 'rdf-c14n';

main() {
    // Any implementation of the data factory will do in the call below.
    const rdfc10 = new RDFC10();  

    const input: string = fetchYourNQuadsDocument();

    // "normalized" is an N-Quads document with all blank nodes canonicalized 
    const normalized: string = rdfc10.canonicalize(input);

    // "hash" is the hash value of the canonical dataset, per specification
    const hash = rdfc10.hash(normalized);
}

Additional features

Choice of hash

The RDFC 1.0 algorithm is based on an extensive usage of hashing. By default, as specified by the document, the hash function is 'sha256'. This default hash function can be changed via the

    rdfc10.hash_algorithm = algorithm;

attribute, where algorithm can be any hash function identification. Examples are 'sha256', 'sha512', etc. The list of available hash algorithms can be retrieved as:

    rdfc10.available_hash_algorithms;

which corresponds to what the underlying OpenSSL library of node.js implements (as of June 2023, i.e., version 18.16.0).

Controlling the recursion level

On rare occasion, the RDFC 1.0 algorithm has to go through some recursive steps. On even more extreme situations, the running of the algorithm could result in an unreasonably long canonicalization process. Although this practically never occurs in practice, attackers may use some "poison graphs" to create such a situation (see the security consideration section in the specification).

This implementation sets a maximum level; this level can be accessed by the

    rdfc10.maximum_allowed_recursion_level;

(read-only) attribute. This number can be lowered by setting the

    rdfc10.maximum_recursion_level

attribute. The value of this attribute cannot exceed the system wide maximum allowed level.

Logging

The canonicalization algorithm has built-in logging points that can be followed via a logger. This is only of interest for debugging the algorithm itself; it can be safely ignored by the average user. By default, no logging happens.

A built-in logger can be switched on which displays logging information in YAML. To use this YAML logger, do the following:

import { LogLevels } from 'rdfjs-c14n';
…
main() {
    …
    const rdfc10 = new RDFC10();
    // `logLevel` may be LogLevels.error, LogLevels.warn, LogLevels.info, LogLevels.debug  
    const logger = rdfc10.setLogger("YamlLogger", logLevel);
    …
    // "logger.log" is a string containing the full log in YAML format
    console.log(logger.log);
}

Implementers may add their own loggers to the system by implementing a new Logger instance. See the interface specification for Logger to possibly implement your own logger, and the general documentation on how to add this logger to the list of available loggers. In case there are more loggers, the list of available loggers is also available to the end user via:

    rdfc10.available_logger_types;

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Maintainer: @iherman