Uniform Data Fingerprint (UDF)
draft-hallambaker-udf-07
The information below is for an old version of the document.
| Document | Type |
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|---|---|---|---|
| Author | Phillip Hallam-Baker | ||
| Last updated | 2017-09-18 | ||
| Replaced by | draft-hallambaker-mesh-udf, draft-hallambaker-mesh-udf | ||
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| Consensus boilerplate | Unknown | ||
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draft-hallambaker-udf-07
Network Working Group P. Hallam-Baker
Internet-Draft Comodo Group Inc.
Intended status: Informational September 18, 2017
Expires: March 22, 2018
Uniform Data Fingerprint (UDF)
draft-hallambaker-udf-07
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 MIME 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.
This document is also available online at
http://prismproof.org/Documents/draft-hallambaker-udf.html [1] .
Status of This Memo
This Internet-Draft is submitted in full conformance with the
provisions of BCP 78 and BCP 79.
Internet-Drafts are working documents of the Internet Engineering
Task Force (IETF). Note that other groups may also distribute
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Internet-Drafts are draft documents valid for a maximum of six months
and may be updated, replaced, or obsoleted by other documents at any
time. It is inappropriate to use Internet-Drafts as reference
material or to cite them other than as "work in progress."
This Internet-Draft will expire on March 22, 2018.
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Copyright Notice
Copyright (c) 2017 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
(https://trustee.ietf.org/license-info) in effect on the date of
publication of this document. Please review these documents
carefully, as they describe your rights and restrictions with respect
to this document. Code Components extracted from this document must
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the Trust Legal Provisions and are provided without warranty as
described in the Simplified BSD License.
Table of Contents
1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . 3
1.1. Algorithm Identifier . . . . . . . . . . . . . . . . . . 4
1.2. Content Type Identifier . . . . . . . . . . . . . . . . . 4
1.3. Representation . . . . . . . . . . . . . . . . . . . . . 5
1.4. Truncation . . . . . . . . . . . . . . . . . . . . . . . 5
2. Definitions . . . . . . . . . . . . . . . . . . . . . . . . . 5
2.1. Requirements Language . . . . . . . . . . . . . . . . . . 5
2.2. Defined Terms . . . . . . . . . . . . . . . . . . . . . . 6
2.3. Related Specifications . . . . . . . . . . . . . . . . . 7
2.4. Implementation Status . . . . . . . . . . . . . . . . . . 7
3. Encoding . . . . . . . . . . . . . . . . . . . . . . . . . . 7
3.1. Binary Fingerprint Value . . . . . . . . . . . . . . . . 7
3.1.1. Version ID . . . . . . . . . . . . . . . . . . . . . 8
3.2. Truncation . . . . . . . . . . . . . . . . . . . . . . . 8
3.3. Base32 Representation . . . . . . . . . . . . . . . . . . 8
3.4. Examples . . . . . . . . . . . . . . . . . . . . . . . . 9
3.4.1. Using SHA-2-512 Digest . . . . . . . . . . . . . . . 9
3.5. Fingerprint Improvement . . . . . . . . . . . . . . . . . 9
3.6. Compressed Presentation . . . . . . . . . . . . . . . . . 9
3.7. Identifiers formed using UDFs . . . . . . . . . . . . . . 10
3.7.1. URI Representation . . . . . . . . . . . . . . . . . 10
3.7.2. DNS Name . . . . . . . . . . . . . . . . . . . . . . 10
4. Content Types . . . . . . . . . . . . . . . . . . . . . . . . 11
4.1. PKIX Certificates and Keys . . . . . . . . . . . . . . . 11
4.2. OpenPGP Key . . . . . . . . . . . . . . . . . . . . . . . 12
4.3. DNSSEC . . . . . . . . . . . . . . . . . . . . . . . . . 12
5. Additional UDF Renderings . . . . . . . . . . . . . . . . . . 12
5.1. Machine Readable Rendering . . . . . . . . . . . . . . . 12
5.2. Word Lists . . . . . . . . . . . . . . . . . . . . . . . 12
5.3. Image List . . . . . . . . . . . . . . . . . . . . . . . 13
6. Security Considerations . . . . . . . . . . . . . . . . . . . 13
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6.1. Work Factor and Precision . . . . . . . . . . . . . . . . 13
6.2. Semantic Substitution . . . . . . . . . . . . . . . . . . 14
7. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 15
7.1. URI Registration . . . . . . . . . . . . . . . . . . . . 15
7.2. Content Type Registration . . . . . . . . . . . . . . . . 15
7.3. Version Registry . . . . . . . . . . . . . . . . . . . . 15
8. References . . . . . . . . . . . . . . . . . . . . . . . . . 15
8.1. Normative References . . . . . . . . . . . . . . . . . . 15
8.2. Informative References . . . . . . . . . . . . . . . . . 15
8.3. URIs . . . . . . . . . . . . . . . . . . . . . . . . . . 16
Author's Address . . . . . . . . . . . . . . . . . . . . . . . . 16
1. Introduction
The use of cryptographic digest functions to produce 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:
o It is easy to compute the digest value for any given message
o It is infeasible to generate a message from its digest value
o It is infeasible to modify a message without changing the digest
value
o 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.
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1.1. 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.
For example the MD5 message digest algorithm [RFC1321] , was widely
used in IETF protocols until it was demonstrated to be vulnerable to
collision attacks [Dobertin95] .
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.
The two digest algorithms currently used in the UDF scheme are both
believed to be strong. These are SHA-2-512 [SHA-2] and SHA-3-512
[SHA-3] . The most secure, 512 bit version of the algorithm is used
in both cases although the output is almost invariably truncated to a
shorter length. Use of the strongest version of the algorithm in
every circumstance eliminates the need to negotiate the algorithm
strength.
1.2. 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.
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1.3. 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.
1.4. 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.
2. Definitions
This section presents the related specifications and standard, the
terms that are used as terms of art within the documents and the
terms used as requirements language.
2.1. 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 [RFC2119].
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2.2. Defined Terms
Cryptographic Digest Function
A hash function that has the properties required for use as a
cryptographic hash function. These include collision resistance,
first pre-image resistance and second pre-image resistance.
Content Type An identifier indicating how a Data Value is to be
interpreted as specified in the IANA registry Media Types.
Data Value The binary octet stream that is the input to the digest
function used to calculate a digest value.
Data Object A Data Value and its associated Content Type
Digest Algorithm A synonym for Cryptographic Digest Function
Digest Value The output of a Cryptographic Digest Function
Data Digest Value The output of a Cryptographic Digest Function for
a given Data Value input.
Fingerprint A presentation of the digest value of a data value or
data object.
Fingerprint Presentation The representation of at least some part of
a fingerprint value in human or machine readable form.
Fingerprint Improvement The practice of recording a higher precision
presentation of a fingerprint on successful validation.
Fingerprint Work Hardening The practice of generating a sequence of
fingerprints until one is found that matches criteria that permit
a compressed presentation form to be used. The compressed
fingerprint thus being shorter than but presenting the same work
factor as an uncompressed one.
Hash A function which takes an input and returns a fixed-size
output. Ideally, the output of a hash function is unbiased and
not correlated to the outputs returned to similar inputs in any
predictable fashion.
Precision The number of significant bits provided by a Fingerprint
Presentation.
Work Factor A measure of the computational effort required to
perform an attack against some security property.
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2.3. Related Specifications
This specification makes use of Base32 [RFC4648] encoding, SHA-2
[SHA-2] and SHA-3 [SHA-3] digest functions.
UDFs are used in the definition of Strong Internet Names
[hallambaker-sin] .
2.4. Implementation Status
The implementation status of the reference code base is described in
the companion document [draft-hallambaker-mesh-developer] .
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>))
Figure 1
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.
Figure 2
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.
The inclusion of a MIME content type prevents message substitution
attacks in which one content type is substituted for another.
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3.1.1. Version ID
A Version Identifier consists of a single byte. The following digest
algorithm identifiers are specified in this document:
+-----------------+------------------------+-----------------+
| Version ID | Algorithm | Reference |
+-----------------+------------------------+-----------------+
| 96 | SHA-2-512 | <norm="SHA-2"/> |
| 97, 98, 99, 100 | SHA-2-512 (compressed) | <norm="SHA-2"/> |
| 144 | SHA-3-512 | <norm="SHA-3"/> |
+-----------------+------------------------+-----------------+
Table 1
These algorithm identifiers have been 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 regardless of the intended output presentation.
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.
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3.4. Examples
In the following examples, <Content-ID> is the UTF8 encoding of the
string "text/plain" and <Data> is the UTF8 encoding of the string
"UDF Data Value"
Data = 55 44 46 20 44 61 74 61 20 56 61 6c 75 65
3.4.1. Using SHA-2-512 Digest
H( <Data> ) =
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( <Data> ) + 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
Figure 3
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. Fingerprint Improvement
Since an application must always calculate the full fingerprint value
as part of the verification process, an application MAY record a
Applications are encouraged to make use of the practice of
fingerprint improvement wherever possible.
3.6. Compressed Presentation
Fingerprint compression permits the use of shorter fingerprint
presentation without a reduction in the attacker work factor by
requiring the fingerprint value to match a particular pattern.
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UDF fingerprints MUST use compression if possible. A compressed
fingerprint uses a version identifier that specifies the form of
compression used as follows:
+------------+-------------------------+
| Version ID | Compression |
+------------+-------------------------+
| 96 | None |
| 97 | First 25 bits are zeros |
| 98 | First 40 bits are zeros |
| 99 | First 50 bits are zeros |
| 100 | First 55 bits are zeros |
+------------+-------------------------+
Table 2
Thus, the fingerprint that would be represented in uncompressed form
as MAAAA-AAWIY-LTMFTG-CZTRO is instead represented as MIWIY-LTMFTG-
CZTRO.
3.7. Identifiers formed using UDFs
UDF fingerprints MAY be used to form a part of another protocol
identifier. Such practice carries the implicit semantic that the
interpretation of the identifier formed is bound to the document
identified by the fingerprint.
3.7.1. URI Representation
Any UDF fingerprint MAY be encoded as a URI by prefixing the Base32
text representation of the fingerprint with the string ?udf:?
3.7.2. DNS Name
A UDF fingerprint MAY be encoded as a DNS label by prefixing the
Base32 text representation with the string ?mm--?.
A DNS name that includes a UDF fingerprint as a DNS label carries the
implicit assertion that the interpretation of the address MUST be
authorized by a security policy that is validated under a key that
matches the corresponding fingerprint.
Placing such a DNS label as the top level (rightmost) label in a DNS
address creates an address that is not legal and thus cannot be
resolved by the Internet DNS infrastructure. Thus ensuring that the
address is rejected by applications that are not capable of
performing the associated validation steps.
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For example, Alice has the email security key with fingerprint MB2GK-
6DUF5-YGYYL-JNY5E. She uses the following email addresses:
alice@example.com Alice publishes this email address when she does
not want the other party to use the secure email system.
alice@mm--mb2gk-6duf5-ygyyl-jny5e.example.com Alice publishes this
email address when she wants to give the other party the option of
using secure email if their system supports it.
The DNS server for example.com has been configured to redirect
requests to resolve zz--mb2gk-6duf5-ygyyl-jny5e.example.com to the
mail server example.com.
alice@example.com.mm--mb2gk-6duf5-ygyyl-jny5e Alice uses this email
address when she wants the other party to be able to send her
email if and only if their client supports use of the secure
messaging system.
While there should never be a DNS label of the form mm--* in the
authoritative DNS root, such labels MAY be introduced by a trusted
local resolver. This would allow attempts at making an untrusted
communication request to be transparently redirected through a
locally trusted security enhancing proxy.
4. Content Types
While a UDF fingerprint MAY be used to identify any form of static
data, the use of a UDF fingerprint to identify a public key signature
key provides a level of indirection and thus the ability to identify
dynamic data. The content types used to identify public keys are
thus of particular interest.
As described in the security considerations section, the use of
fingerprints to identify a bare public key and the use of
fingerprints to identify a public key and associated security policy
information are very different.
4.1. PKIX Certificates and Keys
UDF fingerprints MAY be used to identify PKIX certificates, CRLs and
public keys in the ASN.1 encoding used in PKIX certificates.
Since PKIX certificates and CLRs contain security policy information,
UDF fingerprints used to identify certificates or CRLs SHOULD be
presented with a minimum of 200 bits of precision. PKIX applications
MUST not accept UDF fingerprints specified with less than 200 bits of
precision for purposes of identifying trust anchors.
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PKIX certificates, keys and related content data are identified by
the following content types:
application/pkix-cert A PKIX Certificate
application/pkix-crl A PKIX CRL
application/pkix-keyinfo The KeyInfo structure defined in the PKIX
certificate specification
4.2. OpenPGP Key
OpenPGPv5 keys and key set content data are identified by the
following content types:
application/pgp-key-v5 An OpenPGP key
application/pgp-keys An OpenPGP key set.
4.3. DNSSEC
DNSSEC record data consists of DNS records which are identified by
the following content type:
application/dns A DNS resource record in binary format
5. 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.
5.1. 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.
5.2. 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.
5.3. 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.
6. Security Considerations
6.1. Work Factor and Precision
A given UDF data object has a single fingerprint value that may be
presented at different precisions. The shortest legitimate precision
with which a UDF fingerprint may be presented has 96 significant bits
A UDF fingerprint presents the same work factor as any other
cryptographic digest function. The difficulty of finding a second
data item that matches a given fingerprint is 2^n and the difficulty
or finding two data items that have the same fingerprint is 2^(n/2).
Where n is the precision of the fingerprint.
For the algorithms specified in this document, n = 512 and thus the
work factor for finding collisions is 2^256, a value that is
generally considered to be computationally infeasible.
Since the use of 512 bit fingerprints is impractical in the type of
applications where fingerprints are generally used, truncation is a
practical necessity. The longer a fingerprint is, the less likely it
is that a user will check every character. It is therefore important
to consider carefully whether the security of an application depends
on second pre-image resistance or collision resistance.
In most fingerprint applications, such as the use of fingerprints to
identify public keys, the fact that a malicious party might generate
two keys that have the same fingerprint value is a minor concern.
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Combined with a flawed protocol architecture, such a vulnerability
may permit an attacker to construct a document such that the
signature will be accepted as valid by some parties but not by
others.
For example, Alice generates keypairs until two are generated that
have the same 100 bit UDF presentation (typically 2^48 attempts).
She registers one keypair with a merchant and the other with her
bank. This allows Alice to create a payment instrument that will be
accepted as valid by one and rejected by the other.
The ability to generate of two PKIX certificates with the same
fingerprint and different certificate attributes raises very
different and more serious security concerns. For example, an
attacker might generate two certificates with the same key and
different use constraints. This might allow an attacker to present a
highly constrained certificate that does not present a security risk
to an application for purposes of gaining approval and an
unconstrained certificate to request a malicious action.
In general, any use of fingerprints to identify data that has
security policy semantics requires the risk of collision attacks to
be considered. For this reason the use of short, ?user friendly?
fingerprint presentations (Less than 200 bits) SHOULD only be used
for public key values.
6.2. Semantic Substitution
Many applications record the fact that a data item is trusted, rather
fewer record the circumstances in which the data item is trusted.
This results in a semantic substitution vulnerability which an
attacker may exploit by presenting the trusted data item in the wrong
context.
The UDF format provides protection against high level semantic
substitution attacks by incorporating the content type into the input
to the outermost fingerprint digest function. The work factor for
generating a UDF fingerprint that is valid in both contexts is thus
the same as the work factor for finding a second preimage in the
digest function (2^512 for the specified digest algorithms).
It is thus infeasible to generate a data item such that some
applications will interpret it as a PKIX key and others will accept
as an OpenPGP key. While attempting to parse a PKIX key as an
OpenPGP key is virtually certain to fail to return the correct key
parameters it cannot be assumed that the attempt is guaranteed to
fail with an error message.
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The UDF format does not provide protection against semantic
substitution attacks that do not affect the content type.
7. IANA Considerations
[This will be extended later]
7.1. URI Registration
[Here a URI registration for the udf: scheme]
7.2. Content Type Registration
[application/pkix-keyinfo]
[application/pgp-key]
7.3. Version Registry
96 = SHA-2-512
97 = SHA-2-512 with 25 leading zeros
98 = SHA-2-512 with 40 leading zeros
99 = SHA-2-512 with 50 leading zeros
100 = SHA-2-512 with 55 leading zeros
144 = SHA-3-512
Figure 4
8. References
8.1. Normative References
[RFC4648] Josefsson, S., "The Base16, Base32, and Base64 Data
Encodings", RFC 4648, DOI 10.17487/RFC4648, October 2006.
[SHA-2] NIST, "Secure Hash Standard", August 2015.
[SHA-3] Dworkin, M., "SHA-3 Standard: Permutation-Based Hash and
Extendable-Output Functions", August 2015.
8.2. Informative References
[Dobertin95]
Eurocrypt 1996, "Cryptanalysis of MD5 Compress".
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[draft-hallambaker-mesh-developer]
Hallam-Baker, P., "Mathematical Mesh: Reference
Implementation", draft-hallambaker-mesh-developer-04 (work
in progress), September 2017.
[hallambaker-sin]
"[Reference Not Found!]".
[RFC1321] Rivest, R., "The MD5 Message-Digest Algorithm", RFC 1321,
DOI 10.17487/RFC1321, April 1992.
8.3. URIs
[1] http://prismproof.org/Documents/draft-hallambaker-udf.html
Author's Address
Phillip Hallam-Baker
Comodo Group Inc.
Email: philliph@comodo.com
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