Internet Engineering Task Force (IETF) Phillip Hallam-Baker
INTERNET-DRAFT Comodo Group Inc.
Intended Status: Standards Track May 22, 2015
Expires: November 23, 2015
Uniform Data Fingerprint (UDF)
draft-hallambaker-udf-00
Abstract
This document describes means of generating Uniform Data Fingerprint
(UDF) values and their presentation as text sequences and as URIs.
Cryptographic digests provide a means of uniquely identifying static
data without the need for a registration authority. A fingerprint is
a form of presenting a cryptographic digest that makes it suitable
for use in applications where human readability is required. The UDF
fingerprint format improves over existing formats through the
introduction of a compact algorithm identifier affording an
intentionally limited choice of digest algorithm and the inclusion of
an IANA registered Content-Type identifier within the scope of the
digest input to allow the use of a single fingerprint format in
multiple application domains.
Alternative means of rendering fingerprint values are considered
including machine readable codes, word and image lists.
Status of This Memo
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Copyright Notice
Copyright (c) 2015 IETF Trust and the persons identified as the
document authors. All rights reserved.
This document is subject to BCP 78 and the IETF Trust's Legal
Provisions Relating to IETF Documents
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publication of this document. Please review these documents
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to this document. Code Components extracted from this document must
include Simplified BSD License text as described in Section 4.e of
the Trust Legal Provisions and are provided without warranty as
described in the Simplified BSD License.
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Table of Contents
1. Definitions . . . . . . . . . . . . . . . . . . . . . . . . . 4
1.1. Requirements Language . . . . . . . . . . . . . . . . . . 4
2. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . 4
2.1. Algorithm Identifier . . . . . . . . . . . . . . . . . . 4
2.2. Content Type Identifier . . . . . . . . . . . . . . . . . 5
2.3. Representation . . . . . . . . . . . . . . . . . . . . . 5
2.4. Truncation . . . . . . . . . . . . . . . . . . . . . . . 5
3. Encoding . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
3.1. Binary Fingerprint Value . . . . . . . . . . . . . . . . 6
3.1.1. Version ID . . . . . . . . . . . . . . . . . . . . . 7
3.2. Truncation . . . . . . . . . . . . . . . . . . . . . . . 7
3.3. Base32 Representation . . . . . . . . . . . . . . . . . . 7
3.4. URI Representation . . . . . . . . . . . . . . . . . . . 7
3.5. Examples . . . . . . . . . . . . . . . . . . . . . . . . 7
3.5.1. Using SHA-2-512 Digest . . . . . . . . . . . . . . . 8
3.5.2. Using SHA-3-512 Digest . . . . . . . . . . . . . . . 8
4. Additional UDF Renderings . . . . . . . . . . . . . . . . . . 8
4.1. Machine Readable Rendering . . . . . . . . . . . . . . . 8
4.2. Word Lists . . . . . . . . . . . . . . . . . . . . . . . 8
4.3. Image List . . . . . . . . . . . . . . . . . . . . . . . 9
5. Security Considerations . . . . . . . . . . . . . . . . . . . 9
5.1. Precision . . . . . . . . . . . . . . . . . . . . . . . . 9
5.2. Use of Truncated Digests . . . . . . . . . . . . . . . . 9
6. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 9
6.1. URI Registration . . . . . . . . . . . . . . . . . . . . 9
6.2. Content Type Registration . . . . . . . . . . . . . . . . 9
6.3. Version Registry . . . . . . . . . . . . . . . . . . . . 9
7. References . . . . . . . . . . . . . . . . . . . . . . . . . . 9
7.1. Normative References . . . . . . . . . . . . . . . . . . 9
Author's Address . . . . . . . . . . . . . . . . . . . . . . . . . 10
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1. Definitions
1.1. Requirements Language
The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT",
"SHOULD", "SHOULD NOT", "RECOMMENDED", "MAY", and "OPTIONAL" in this
document are to be interpreted as described in RFC 2119 [RFC2119].
2. Introduction
The use of cryptographic digest values as identifiers is well
established as a means of generating a unique identifier for fixed
data without the need for a registration authority.
While the use of fingerprints of public keys was popularized by PGP,
they are employed in many other applications including OpenPGP, SSH,
BitCoin and PKIX.
A cryptographic digest is a particular form of hash function that has
the properties:
* The digest function transforms an arbitrary message to a
fixed size digest value.
* It is easy to compute the digest value for any given message
* It is?infeasible?to generate a message from its digest value
* It is infeasible to modify a message without changing the
digest value
* It is infeasible to find two different messages with the
same digest value.
If these properties are met, the only way that two data objects that
map to the same digest value is by random chance. If the number of
possible digest values is sufficiently large (i.e. is a sufficiently
large number of bits in length), this chance is reduced to an
arbitrarily infinitesimal probability. Such values are described as
being probabilistically unique.
A fingerprint is a representation of a cryptographic digest value
optimized for purposes of verification and in some cases data entry.
2.1. Algorithm Identifier
Although a secure cryptographic digest algorithm has properties that
make it ideal for certain types of identifier use, several
cryptographic digest algorithms have found widespread use, some of
which have been demonstrated to be insecure.
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For example the MD5 message digest algorithm [RFC1321], was widely
used in IETF protocols until it was demonstrated to be vulnerable to
collision attacks [TBS].
The secure use of a fingerprint scheme therefore requires the digest
algorithm to either be fixed or otherwise determined by the
fingerprint value itself. Otherwise an attacker may be able to use a
weak, broken digest algorithm to generate a data object matching a
fingerprint value generated using a strong digest algorithm.
2.2. Content Type Identifier
A secure cryptographic digest algorithm provides a unique digest
value that is probabilistically unique for a particular byte sequence
but does not fix the context in which a byte sequence is interpreted.
While such ambiguity may be tolerated in a fingerprint format
designed for a single specific field of use, it is not acceptable in
a general purpose format.
For example, the SSH and OpenPGP applications both make use of
fingerprints as identifiers for the public keys used but using
different digest algorithms and data formats for representing the
public key data. While no such vulnerability has been demonstrated to
date, it is certainly conceivable that a crafty attacker might
construct an SSH key in such a fashion that OpenPGP interprets the
data in an insecure fashion. If the number of applications making use
of fingerprint format that permits such substitutions is sufficiently
large, the probability of a semantic substitution vulnerability being
possible becomes unacceptably large.
A simple control that defeats such attacks is to incorporate a
content type identifier within the scope of the data input to the
hash function.
2.3. Representation
The representation of a fingerprint is the format in which it is
presented to either an application or the user.
Base32 encoding is used to produce the preferred text representation
of a UDF fingerprint. This encoding uses only the letters of the
Latin alphabet with numbers chosen to minimize the risk of ambiguity
between numbers and letters (2, 3, 4, 5, 6 and 7).
To enhance readability and improve data entry, characters are grouped
into groups of five.
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2.4. Truncation
Different applications of fingerprints demand different tradeoffs
between compactness of the representation and the number of
significant bits. A larger the number of significant bits reduces the
risk of collision but at a cost to convenience.
Modern cryptographic digest functions such as SHA-2 produce output
values of at least 256 bits in length. This is considerably larger
than most uses of fingerprints require and certainly greater than can
be represented in human readable form on a business card.
Since a strong cryptographic digest function produces an output value
in which every bit in the input value affects every bit in the output
value with equal probability, it follows that truncating the digest
value to produce a finger print is at least as strong as any other
mechanism if digest algorithm used is strong.
Using truncation to reduce the precision of the digest function has
the advantage that a lower precision fingerprint of some data content
is always a prefix of a higher prefix of the same content. This
allows higher precision fingerprints to be converted to a lower
precision without the need for special tools.
3. Encoding
A UDF fingerprint for a given data object is generated by calculating
the Binary Fingerprint Value for the given data object and type
identifier, truncating it to obtain the desired degree of precision
and then converting the truncated value to a representation.
3.1. Binary Fingerprint Value
The binary encoding of a fingerprint is calculated using the formula:
Fingerprint = Version-ID + H ( Content-ID + ':' + H(Data))
Where
H(x) is the cryptographic digest function
Version-ID is the fingerprint version and algorithm identifier.
Content-ID is the MIME Content-Type of the data.
Data is the binary data.
The use of the nested hash function permits a fingerprint to be taken
of data for which a digest value is already known without the need to
calculate a new digest over the data.
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The inclusion of a MIME content type prevents message substitution
attacks in which one content type is substituted for another.
3.1.1. Version ID
Two digest algorithm identifiers are specified in this document:
SHA-2-512 = 96
SHA-3-512 = 144
These algorithm identifiers have been carefully chosen so that the
first character in a SHA-2-512 fingerprint will always be 'M' and the
first character in a SHA-3-512 fingerprint will always be 'S'. These
provide mnemonics for 'Merkle-Damgard' and 'Sponge' respectively.
3.2. Truncation
The Binary Fingerprint Value is truncated to an integer multiple of
25 bits bits regardless of the intended output representation.
The output of the hash function is truncated to a sequence of n bits
by first selecting the first n/8 bytes of the output function. If n
is an integer multiple of 8, no additional bits are required and this
is the result. Otherwise the remaining bits are taken from the most
significant bits of the next byte and any unused bits set to 0.
For example, to truncate the byte sequence [a0, b1, c2, d3, e4] to 25
bits. 25/8 = 3 bytes with 1 bit remaining, the first three bytes of
the truncated sequence is [a0, b1, c2] and the final byte is e4 AND
80 = 80 which we add to the previous result to obtain the final
truncated sequence of [a0, b1, c2, 80]
3.3. Base32 Representation
A modified version of Base32 [RFC4648] encoding is used to present
the fingerprint in text form grouping the output text into groups of
five characters separated by a dash '-'. This representation improves
the accuracy of both data entry and verification.
3.4. URI Representation
Any UDF fingerprint MAY be encoded as a URI by prefixing the Base32
text representation of the fingerprint with the string 'udf:'
3.5. Examples
In the following examples, <Content-ID> is the UTF8 encoding of the
string "text/plain" and is the UTF8 encoding of the string "UDF Data
Value"
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Data = 55 44 46 20 44 61 74 61 20 56 61 6c 75 65
3.5.1. Using SHA-2-512 Digest
H( ) =
48 da 47 cc ab fe a4 5c 76 61 d3 21 ba 34 3e 58
10 87 2a 03 b4 02 9d ab 84 7c ce d2 22 b6 9c ab
02 38 d4 e9 1e 2f 6b 36 a0 9e ed 11 09 8a ea ac
99 d9 e0 bd ea 47 93 15 bd 7a e9 e1 2e ad c4 15
H(H( ) + Content-ID>) =
45 e0 59 e0 39 34 ea b7 f6 5d 83 b2 d8 f9 b1 6d
2a 6b 08 63 d9 3c c1 02 86 7b 83 49 f2 d9 f0 8f
fe 07 87 30 c7 c9 05 74 ac a1 38 2b b3 14 4d c6
39 f9 8c 12 c0 4a 3e b5 05 0b 3e 67 df 52 4b 57
Text Presentation (100 bit) MB2GK-6DUF5-YGYYL-JNY5E
Text Presentation (125 bit) MB2GK-6DUF5-YGYYL-JNY5E-RWSHZ
Text Presentation (150bit) MB2GK-6DUF5-YGYYL-JNY5E-RWSHZ-SV75J
Text Presentation (250bit) MB2GK-6DUF5-YGYYL-JNY5E-RWSHZ-SV75J-C4OZQ-
5GIN2-GQ7FQ-EEHFI
3.5.2. Using SHA-3-512 Digest
[This data intentionally omitted pending publication of the final
SHA-3 standards document]
4. Additional UDF Renderings
By default, a UDF fingerprint is rendered in the Base32 encoding
described in this document. Additional renderings MAY be employed to
facilitate entry and/or verification of fingerprint values.
4.1. Machine Readable Rendering
The use of a machine readable rendering such as a QR Code allows a
UDF value to be input directly using a smartphone or other device
equipped with a camera.
A QR code fixed to a network capable device might contain the
fingerprint of a machine readable description of the device.
4.2. Word Lists
The use of a Word List to encode fingerprint values was introduced by
Patrick Juola and Philip Zimmerman for the PGPfone application. The
PGP Word List is designed to facilitate exchange and verification of
fingerprint values in a voice application. To minimize the risk of
misinterpretation, two word lists of 256 values each are used to
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encode alternative fingerprint bytes. The compact size of the lists
used allowed the compilers to curate them so as to maximize the
phonetic distance of the words selected.
The PGP Word List is designed to achieve a balance between ease of
entry and verification. Applications where only verification is
required may be better served by a much larger word list, permitting
shorter fingerprint encodings.
For example, a word list with 16384 entries permits 14 bits of the
fingerprint to be encoded at once, 65536 entries permits 16. These
encodings allow a 125 bit fingerprint to be encoded in 9 and 8 words
respectively.
4.3. Image List
An image list is used in the same manner as a word list affording
rapid visual verification of a fingerprint value. For obvious
reasons, this approach is not generally suited to data entry.
5. Security Considerations
5.1. Precision
5.2. Use of Truncated Digests
6. IANA Considerations
[This will be extended later]
6.1. URI Registration
[Here a URI registration for the udf: scheme]
6.2. Content Type Registration
[PKIX KeyInfo]
[PGP Key Packet]
6.3. Version Registry
96 = SHA-2-512
144 = SHA-3-512
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7. References
7.1. Normative References
[RFC1321] Rivest, R., "The MD5 Message-Digest Algorithm", RFC 1321,
April 1992.
[RFC4648] Josefsson, S., "The Base16, Base32, and Base64 Data
Encodings", RFC 4648, October 2006.
Author's Address
Phillip Hallam-Baker
Comodo Group Inc.
philliph@comodo.com
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