Data Encryption Based On Protein Synthesis - Nguyen Dang Binh

Figure 3: Illustration of first method - One Bite of
data is translated to one Bite of code.
Figure 4: Illustration of second method - One Bite
of data is translated to a string.
In Figures 5-a, b, these two coding methods are
compared in term of their coding structure.
Figure 5-a: indicates the table of unique codes
based on first method.
Elements including: 00, 01, 10 and 11.
Figure 5-b: indicates the table of unique codes
based on second method.
Elements including: A, H, K and M
3.3 The transmission
Since data communication in digital systems is in
the form of 0 and 1, the first method well complies
with these communication channels. However,
because the second method produces the alphabetic
outputs rather than binary digits, additional encoding
is employed to transform alphabets to binary system
while improving security. This is carried out by
Huffman coding.
4 Required background of Huffman
Code
Huffman Coding is a lossless method of
compressing data and a form of entropy encoding
[1, 6]. Lossless data compression is an algorithm
where original data can be reconstructed from the
compressed data. Entropy encoding is lossless data
compression which assigns codes to symbols with
the length of each codeword proportional to the
frequency of that symbol [4, 7]. Specifically Huffman
codes are variable-length codes; shorter codeword
are assigned to symbols with the highest frequency.
This has an advantage over fixed-length codes as
the average overall bits produced after encoding the
data using variable-length codes, is fewer than
encoding the same data using fixed-length codes.
4.1 Encoding using Huffman Tree
The technique works by creating a binary tree of
nodes. This is done first by considering each symbol
to be encoded as a single tree, each consisting of
a single node. Each node is shown by the frequency
of its symbol. The trees which sum of their root
nodes is the least total frequency among other trees
are selected, “producing” the sub trees of a new root
which is the sum of the frequencies of the sub trees.
The sub trees are then removed from the forest.
This process is recursive and continues until only
one tree -The Huffman Encoding Tree- remains.
Each level is represented using one bit code, 0 or 1.
Classically a value of 0 is associated with an edge to
any left child and a value of 1 with an edge to any
right child .Thus the most frequent symbols’ codes
have fewer bits as they are nearer to the root [1].
There are two variations for constructing Huffman
codes: arbitrary and right-heavy Huffman coding. In
the former, after combining two least frequent
symbols as sub trees of the new root, the decision is
arbitrary as to assign which sub tree as the right or
left child of the root. However in the latter algorithm
this is always the sub tree having greater frequency
assigned as the right child. “By concatenating the
labels associated with the edges that make up the
path from the root to a leaf, we get a binary string.
Thus the mapping is defined [1].”