Abstract: also eases the transaction time. The multi level

Abstract: Data security and data compression is much
helpful for effective data management. Several applications concentrating on
the multi level data security and data compression process. The compression
process saves storage space and makes the transmission easier. Huffman coding
is the well known and popular compression technique widely used for text data
compression. Among the lossy and lossless data compression techniques, Huffman
code treated as an optimal solution for secured data compression. This paper gives
a comprehensive analysis and of existing data compression techniques, which are
related to the Huffman code is presented. And this survey also provides
direction to solve problems of such systems.

Index
Terms: Data Compression, data Security, Huffman coding, lossless data
compression.

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1.      Introduction:

Digital Information flow became very huge and
occupied more storage spaces due to wide range of internet applications. Due to
the continuous growth of data size, it is difficult to handle and access. So,
data compression 1 is a best method to achieve high security and reduce
storage space. And it also eases the transaction time. The multi level security
and compression can be performed on different file formats such as text, image,
audio; video etc. this paper gives an analysis on text data security and
compression techniques. Multi level security and data compression have several
research directions. This paper flows on Huffman coding based text compression
schemes. The figure 1.0 shows the process flow involved with the data security
and compression.

Fig
1.0 data compression and decompression

The data compression techniques are
categorized into two types such as Lossy compression and lossless compression.
Several authors have described about these compression techniques. Among loss
and lossless 2, lossy compression is an effective technique. However, this is
effective when the compression made on images and audios.  This paper gives the popular approaches and
detailed survey about lossless compression techniques used for the data
compression.

1.1
Data Compression Techniques:

Data compression and transmission consist of
two steps such as modeling and coding, it takes the stream of symbols and
transform into codes. The stream code size is determined the effectiveness of
compression. If the stream of code is smaller than the original, then the
compression is effective. Lossless data compression is generally implemented using
one of the two different types of modeling namely statistical and
dictionary-based. Statistical
modeling reads in and encodes a single symbol at a time using the probability
of that character’s appearance. Dictionary-based modeling uses a single code to
replace strings of
symbols. In dictionary modeling, the coding problem reduced in significance
leaving the model supremely important. The very popular methods for effective
data            compression is Huffman
coding, Adaptive Huffman coding, Arithmetic encoding, Shannon entropy, Run-Length
encoding and so on 3. However, there are numerous techniques available,
currently a lot of researches commencing for better approach for securing and
compressing the text data. This is very optimal when the data performed encryption,
decryption and also to compression of the text data.

1.2
Huffman Compression Techniques:

            In
lossless data compression, Huffman code 4 is the popular and effective
technique which follows a prefix code generation process. This technique
creates a binary tree and generates different symbols with probability. In
Huffman encoding an unique prefix code is assigned to each symbol The Huffman
compression techniques are two types, one is static and another one is dynamic,
where the static Huffman coding initially calculates the frequency and reads
the content again to compress. So, the static Huffman code compression reads
the data twice. Whereas the dynamic Huffman code initiated with the empty
Huffman tree and modifies it as symbols. The compression and decompression will
change the tree in a same way that used for the compression. Huffman decoding
can start from any point as it is based on codeword for each symbol. Network
related applications can use the Huffman compression technique.

2.      Literature
survey

Data security and compression of text data is
studied together in recent approaches. In this section, some text data security
using Huffman encoding and bit stuffing mechanisms are discussed. A significant
amount of researches concentrated on research related process such as to
encryption, decryption and compression of text data. Some of the related works
are summarized in the following.

            In paper 5, authors Gulhane, suraj
et al proposed a technique with the dynamic Huffman coding scheme for secure
and speed data retrieval. Authors have implemented the concept of DDAS (A
Distributed Data Aggregation Service) using Kerberos. This technique increases
the security and it ensures the only authorized client is able to access
distributed database and for compression and decompression of a data method.
This improved the security and data retrieval using the adaptive Huffman
coding.

            In the paper 6 authors Subhra J.
Sarkar  et al, discussed about the data
storage and security problems. The authors used Huffman Coding based data compression technique.
The technique is improves the security and reduces the size of high dimensional
data array.

            In the paper 7, authors Hameed, Maan,
performed an effective compression of text data by applying the lossless method
of Huffman coding. This technique has achieved fast data compression and
converts into confidential data array.

            Finally authors in paper 8
developed a multilevel security and data compression technique by applying
Huffman coding and bit stuffing algorithms. Authors have implemented and prove
the bit stuff and Huffman coding can provide high level security and high
performance on compression processes. The data compression technique reduces
the transmission time and bandwidth utilization.

Conclusion:

            Data security and storage reduction tasks
are more important in the current trend. The analysis of encoding techniques
and tools for compression is discussed. This paper specifically concentrated on
the Huffman coding related works and its drawbacks. The survey gives the
technique of existing compression techniques for text data. However, the
Huffman coding compression is popular, but the execution issues arises. This
paper gives idea about such issues in brief. From these analysis, an optimal
solution can be found.

References:

1. Sayood, Khalid. Introduction to data
compression. Morgan Kaufmann, 2017.

2. Chang,
Weiling, Binxing Fang, Xiaochun Yun, and Shupeng Wang. “The block lossless
data compression algorithm.” International Journal of Computer
Science and Network Security (IJCSNS) 9, no. 10 (2009): 116.

3. Sharma, Neha, Jasmeet Kaur, and Navmeet Kaur.
“A review on various Lossless text data compression
techniques.” International Journal of Engineering Sciences, Issue 2
(2014).

4. Chau, Savio N., and Ridwan Rashid. “Data
compression with Huffman code on multicore processors.” U.S. Patent
9,258,013, issued February 9, 2016.

5. Gulhane,
Suraj, and Sonali Bodkhe. “DDAS using Kerberos with Adaptive Huffman
Coding to enhance data retrieval speed and security.” In Pervasive
Computing (ICPC), 2015 International Conference on, pp. 1-6. IEEE, 2015.

6. Sarkar,
Subhra J., Nabendu Kr Sarkar, and Antra Banerjee. “A novel Huffman coding
based approach to reduce the size of large data array.” In Circuit,
Power and Computing Technologies (ICCPCT), 2016 International Conference on,
pp. 1-5. IEEE, 2016.

7. Hameed, Maan, Asem Khmag, Fakhrul Zaman, and
Abd Rahman Ramli. “A New Lossless Method of Huffman Coding for Text Data
Compression and Decompression Process with FPGA Implementation.” Journal
of Engineering and Applied Sciences 100, no. 3 (2016): 402-407.

8. Kodabagi, M. M., M. V. Jerabandi, and Nagaraj
Gadagin. “Multilevel security and compression of text data using bit
stuffing and huffman coding.” In Applied and Theoretical Computing
and Communication Technology (iCATccT), 2015 International Conference on,
pp. 800-804. IEEE, 2015.

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