Binary Encodings for JavaScript Object Notation: JSON-B, JSON-C, JSON-D
draft-hallambaker-jsonbcd-09

Abstract

Three binary encodings for JavaScript Object Notation (JSON) are presented. JSON-B (Binary) is a strict superset of the JSON encoding that permits efficient binary encoding of intrinsic JavaScript data types. JSON-C (Compact) is a strict superset of JSON-B that supports compact representation of repeated data strings with short numeric codes. JSON-D (Data) supports additional binary data types for integer and floating-point representations for use in scientific applications where conversion between binary and decimal representations would cause a loss of precision.

This document is also available online at http://prismproof.org/Documents/draft-hallambaker-jsonbcd.html .

Status of This Memo

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This Internet-Draft will expire on March 22, 2018.

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Table of Contents

• 1. Introduction
• 1.1. Objectives
• 2. Definitions
• 2.1. Requirements Language
• 2.2. Defined Terms
• 2.3. Related Specifications
• 2.4. Terminology
• 3. Extended JSON Grammar
• 4. JSON-B
• 4.1. JSON-B Examples
• 5. JSON-C
• 5.1. JSON-C Examples
• 6. JSON-D (Data)
• 7. Acknowledgements
• 8. Security Considerations
• 9. IANA Considerations
• 10. References
• 10.1. Normative References
• 10.2. Informative References
• Author's Address

1. Introduction

JavaScript Object Notation (JSON) is a simple text encoding for the JavaScript Data model that has found wide application beyond its original field of use. In particular JSON has rapidly become a preferred encoding for Web Services.

JSON encoding supports just four fundamental data types (integer, floating point, string and boolean), arrays and objects which consist of a list of tag-value pairs.

Although the JSON encoding is sufficient for many purposes it is not always efficient. In particular there is no efficient representation for blocks of binary data. Use of base64 encoding increases data volume by 33%. This overhead increases exponentially in applications where nested binary encodings are required making use of JSON encoding unsatisfactory in cryptographic applications where nested binary structures are frequently required.

Another source of inefficiency in JSON encoding is the repeated occurrence of object tags. A JSON encoding containing an array of a hundred objects such as {"first":1,"second":2} will contain a hundred occurrences of the string "first" (seven bytes) and a hundred occurrences of the string "second" (eight bytes). Using two byte code sequences in place of strings allows a saving of 11 bytes per object without loss of information, a saving of 50%.

A third objection to the use of JSON encoding is that floating point numbers can only be represented in decimal form and this necessarily involves a loss of precision when converting between binary and decimal representations. While such issues are rarely important in network applications they can be critical in scientific applications. It is not acceptable for saving and restoring a data set to change the result of a calculation.

1.1. Objectives

The following were identified as core objectives for a binary JSON encoding:

• Easy to convert existing encoders and decoders to add binary support
• Efficient encoding of binary data
• Ability to convert from JSON to binary encoding in a streaming mode (i.e. without reading the entire binary data block before beginning encoding.
• Lossless encoding of JavaScript data types
• The ability to support JSON tag compression and extended data types are considered desirable but not essential for typical network applications.

Three binary encodings are defined:

JSON-B (Binary)

Encodes JSON data in binary. Only the JavaScript data model is supported (i.e. atomic types are integers, double or string). Integers may be 8, 16, 32 or 64 bits either signed or unsigned. Floating points are IEEE 754 binary64 format [IEEE754] . Supports chunked encoding for binary and UTF-8 string types.

JSON-C (Compact)

As JSON-B but with support for representing JSON tags in numeric code form (16 bit code space). This is done for both compact encoding and to allow simplification of encoders/decoders in constrained environments. Codes may be defined inline or by reference to a known dictionary of codes referenced via a digest value.

JSON-D (Data)

As JSON-C but with support for representing additional data types without loss of precision. In particular other IEEE 754 floating point formats, both binary and decimal and Intel's 80 bit floating point, plus 128 bit integers and bignum integers.

Each encoding is a proper superset of JSON, JSON-C is a proper superset of JSON-B and JSON-D is a proper superset of JSON-C. Thus a single decoder MAY be used for all three new encodings and for JSON. Figure 1 shows these relationships graphically:

[[This figure is not viewable in this format. The figure is available at http://prismproof.org/Documents/draft-hallambaker-jsonbcd.html.]]

Encoding Relationships.

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].

2.2. Defined Terms

The terms of art used in this document are described in the Mesh Architecture Guide [draft-hallambaker-mesh-architecture] .

2.3. Related Specifications

The JSON-B, JSON-C and JSON-D encodings are all based on the JSON grammar [RFC7159] . IEEE 754 Floating Point Standard is used for encoding floating point numbers [IEEE754] ,

2.4. Terminology

No new terms of art are defined

3. Extended JSON Grammar

The JSON-B, JSON-C and JSON-D encodings are all based on the JSON grammar [RFC7159] using the same syntactic structure but different lexical encodings.

JSON-B0 and JSON-C0 replace the JSON lexical encodings for strings and numbers with binary encodings. JSON-B1 and JSON-C1 allow either lexical encoding to be used. Thus any valid JSON encoding is a valid JSON-B1 or JSON-C1 encoding.

The grammar of JSON-B, JSON-C and JSON-D is a superset of the JSON grammar. The following productions are added to the grammar:

x-value

Binary encodings for data values. As the binary value encodings are all self delimiting

x-member

An object member where the value is specified as an X-value and thus does not require a value-separator.

b-value

Binary data encodings defined in JSON-B.

b-string

Defined length string encoding defined in JSON-B.

c-def

Tag code definition defined in JSON-C. These may only appear before the beginning of an Object or Array and before any preceding white space.

c-tag

Tag code value defined in JSON-C.

d-value

Additional binary data encodings defined in JSON-D for use in scientific data applications.

The JSON grammar is modified to permit the use of x-value productions in place of ( value value-separator ) :

JSON-text = (object / array)
   
object = *cdef begin-object [
         *( member value-separator | x-member )
         (member | x-member) ] end-object
   
member = tag value
x-member = tag x-value
   
tag = string name-separator | b-string | c-tag
 
array = *cdef begin-array [  *( value value-separator | x-value )
(value | x-value) ] end-array
   
x-value = b-value / d-value
   
value = false / null / true / object / array / number / string
   
name-separator  = ws %x3A ws  ; : colon
value-separator = ws %x2C ws  ; , comma

Figure 1

The following lexical values are unchanged:
begin-array     = ws %x5B ws  ; [ left square bracket
begin-object    = ws %x7B ws  ; { left curly bracket
end-array       = ws %x5D ws  ; ] right square bracket
end-object      = ws %x7D ws  ; } right curly bracket
   
ws = *( %x20 %x09 %x0A  %x0D )
   
false = %x66.61.6c.73.65   ; false
null  = %x6e.75.6c.6c      ; null
true  = %x74.72.75.65      ; true

Figure 2

The productions number and string are defined as before:

number = [ minus ] int [ frac ] [ exp ]
decimal-point = %x2E       ; .
digit1-9 = %x31-39         ; 1-9
e = %x65 / %x45            ; e E
exp = e [ minus / plus ] 1*DIGIT
frac = decimal-point 1*DIGIT
int = zero / ( digit1-9 *DIGIT )
minus = %x2D               ; -
plus = %x2B                ; +
zero = %x30                ; 0
   
string = quotation-mark *char quotation-mark
char = unescaped /
escape ( %x22 / %x5C / %x2F / %x62 / %x66 /
%x6E / %x72 / %x74 /  %x75 4HEXDIG )
   
escape = %x5C              ; \
quotation-mark = %x22      ; "
unescaped = %x20-21 / %x23-5B / %x5D-10FFFF

Figure 3

4. JSON-B

The JSON-B encoding defines the b-value and b-string productions:

b-value = b-atom | b-string | b-data | b-integer |
b-float
   
b-string = *( string-chunk ) string-term
b-data = *( data-chunk ) data-last
   
b-integer = p-int8 | p-int16 | p-int32 | p-int64 | p-bignum16 |
n-int8 | n-int16 | n-int32 | n-int64 | n-bignum16
   
b-float = binary64

Figure 4

The lexical encodings of the productions are defined in the following tables where the column 'tag' specifies the byte code that begins the production, 'Fixed' specifies the number of data bytes that follow and 'Length' specifies the number of bytes used to define the length of a variable length field following the data bytes:

Table 1: Codes for String and Data items

Table 2: Codes for Integers, 64 Bit Floating Point, Boolean and Null items.

A data type commonly used in networking that is not defined in this scheme is a datetime representation. To define such a data type, a string containing a date-time value in Internet type format is typically used.

4.1. JSON-B Examples

The following examples show examples of using JSON-B encoding:

A0 2A                            42 (as 8 bit integer)
A1 00 2A                         42 (as 16 bit integer)
A2 00 00 00 2A                   42 (as 32 bit integer)
A3 00 00 00 00 00 00 00 2A       42 (as 64 bit integer)
A5 00 01 42                      42 (as Bignum)
   
80 05 48 65 6c 6c 6f             "Hello" (single chunk)
81 00 05 48 65 6c 6c 6f          "Hello" (single chunk)
84 05 48 65 6c 6c 6f 80 00       "Hello" (as two chunks)
   
92 3f f0 00 00 00 00 00 00       1.0
92 40 24 00 00 00 00 00 00       10.0
92 40 09 21 fb 54 44 2e ea       3.14159265359
92 bf f0 00 00 00 00 00 00       -1.0
   
B0                               true
B1                               false
B2                               null

Figure 5

5. JSON-C

JSON-C (Compressed) permits numeric code values to be substituted for strings and binary data. Tag codes MAY be 8, 16 or 32 bits long encoded in network byte order.

Tag codes MUST be defined before they are referenced. A Tag code MAY be defined before the corresponding data or string value is used or at the same time that it is used.

A dictionary is a list of tag code definitions. An encoding MAY incorporate definitions from a dictionary using the dict-hash production. The dict hash production specifies a (positive) offset value to be added to the entries in the dictionary followed by the UDF fingerprint [draft-hallambaker-udf] of the dictionary to be used.

Table 3: Codes Used for Compression

All integer values are encoded in Network Byte Order (most significant byte first).

5.1. JSON-C Examples

The following examples show examples of using JSON-C encoding:

C8 20 80 05 48 65 6c 6c 6f       "Hello"    20 = "Hello"
C4 21 80 05 48 65 6c 6c 6f                  21 = "Hello"
C0 20                            "Hello"
C1 00 20                         "Hello"
   
D0 00 00 01 00 20             Insert dictionary at code 256
e3 b0 c4 42 98 fc 1c 14
9a fb f4 c8 99 6f b9 24
27 ae 41 e4 64 9b 93 4c
a4 95 99 1b 78 52 b8 55       UDF (C4 21 80 05 48 65 6c 6c 6f)

Figure 6

6. JSON-D (Data)

JSON-B and JSON-C only support the two numeric types defined in the JavaScript data model: Integers and 64 bit floating point values. JSON-D (Data) defines binary encodings for additional data types that are commonly used in scientific applications. These comprise positive and negative 128 bit integers, six additional floating point representations defined by IEEE 754 [IEEE754] and the Intel extended precision 80 bit floating point representation [INTEL] .

Should the need arise, even bigger bignums could be defined with the length specified as a 32 bit value permitting bignums of up to 2^35 bits to be represented.

d-value = d-integer | d-float
   
d-float = binary16 | binary32 | binary128 | binary80 |
decimal32 | decimal64 | decimal 128

Figure 7

The codes for these values are as follows:

Table 4: Additional Codes for Scientific Data

7. Acknowledgements

This work was assisted by conversations with Nico Williams and other participants on the applications area mailing list.

8. Security Considerations

A correctly implemented data encoding mechanism should not introduce new security vulnerabilities. However, experience demonstrates that some data encoding approaches are more prone to introduce vulnerabilities when incorrectly implemented than others.

In particular, whenever variable length data formats are used, the possibility of a buffer overrun vulnerability is introduced. While best practice suggests that a coding language with native mechanisms for bounds checking is the best protection against such errors, such approaches are not always followed. While such vulnerabilities are most commonly seen in the design of decoders, it is possible for the same vulnerabilities to be exploited in encoders.

A common source of such errors is the case where nested length encodings are used. For example, a decoder relies on an outermost length encoding that specifies a length on 50 bytes to allocate memory for the entire result and then attempts to copy a string with a declared length of 1000 bytes within the sequence.

The extensions to the JSON encoding described in this document are designed to avoid such errors. Length encodings are only used to define the length of x-value constructions which are always terminal and cannot have nested data entries.

9. IANA Considerations

[TBS list out all the code points that require an IANA registration]

10. References

10.1. Normative References

10.2. Informative References

Author's Address