Abstract: text data compression. Among the lossy and

Abstract: Data security and data compression is muchhelpful for effective data management. Several applications concentrating onthe multi level data security and data compression process. The compressionprocess saves storage space and makes the transmission easier.

Huffman codingis the well known and popular compression technique widely used for text datacompression. Among the lossy and lossless data compression techniques, Huffmancode treated as an optimal solution for secured data compression. This paper givesa comprehensive analysis and of existing data compression techniques, which arerelated to the Huffman code is presented. And this survey also providesdirection to solve problems of such systems.IndexTerms: Data Compression, data Security, Huffman coding, lossless datacompression.1.      Introduction:Digital Information flow became very huge andoccupied more storage spaces due to wide range of internet applications. Due tothe continuous growth of data size, it is difficult to handle and access.

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So,data compression 1 is a best method to achieve high security and reducestorage space. And it also eases the transaction time. The multi level securityand compression can be performed on different file formats such as text, image,audio; video etc. this paper gives an analysis on text data security andcompression techniques. Multi level security and data compression have severalresearch directions.

This paper flows on Huffman coding based text compressionschemes. The figure 1.0 shows the process flow involved with the data securityand compression.Fig1.

0 data compression and decompressionThe data compression techniques arecategorized into two types such as Lossy compression and lossless compression.Several authors have described about these compression techniques. Among lossand lossless 2, lossy compression is an effective technique. However, this iseffective when the compression made on images and audios.  This paper gives the popular approaches anddetailed survey about lossless compression techniques used for the datacompression.1.1Data Compression Techniques:Data compression and transmission consist oftwo steps such as modeling and coding, it takes the stream of symbols andtransform into codes.

The stream code size is determined the effectiveness ofcompression. If the stream of code is smaller than the original, then thecompression is effective. Lossless data compression is generally implemented usingone of the two different types of modeling namely statistical anddictionary-based. Statisticalmodeling reads in and encodes a single symbol at a time using the probabilityof that character’s appearance.

Dictionary-based modeling uses a single code toreplace strings ofsymbols. In dictionary modeling, the coding problem reduced in significanceleaving the model supremely important. The very popular methods for effectivedata            compression is Huffmancoding, Adaptive Huffman coding, Arithmetic encoding, Shannon entropy, Run-Lengthencoding and so on 3.

However, there are numerous techniques available,currently a lot of researches commencing for better approach for securing andcompressing the text data. This is very optimal when the data performed encryption,decryption and also to compression of the text data.1.

2Huffman Compression Techniques:            Inlossless data compression, Huffman code 4 is the popular and effectivetechnique which follows a prefix code generation process. This techniquecreates a binary tree and generates different symbols with probability. InHuffman encoding an unique prefix code is assigned to each symbol The Huffmancompression techniques are two types, one is static and another one is dynamic,where the static Huffman coding initially calculates the frequency and readsthe content again to compress. So, the static Huffman code compression readsthe data twice. Whereas the dynamic Huffman code initiated with the emptyHuffman tree and modifies it as symbols. The compression and decompression willchange the tree in a same way that used for the compression. Huffman decodingcan start from any point as it is based on codeword for each symbol.

Networkrelated applications can use the Huffman compression technique. 2.      Literaturesurvey Data security and compression of text data isstudied together in recent approaches. In this section, some text data securityusing Huffman encoding and bit stuffing mechanisms are discussed.

A significantamount of researches concentrated on research related process such as toencryption, decryption and compression of text data. Some of the related worksare summarized in the following.            In paper 5, authors Gulhane, surajet al proposed a technique with the dynamic Huffman coding scheme for secureand speed data retrieval. Authors have implemented the concept of DDAS (ADistributed Data Aggregation Service) using Kerberos. This technique increasesthe security and it ensures the only authorized client is able to accessdistributed database and for compression and decompression of a data method.This improved the security and data retrieval using the adaptive Huffmancoding.

            In the paper 6 authors Subhra J.Sarkar  et al, discussed about the datastorage and security problems. The authors used Huffman Coding based data compression technique.

The technique is improves the security and reduces the size of high dimensionaldata array.            In the paper 7, authors Hameed, Maan,performed an effective compression of text data by applying the lossless methodof Huffman coding. This technique has achieved fast data compression andconverts into confidential data array.            Finally authors in paper 8developed a multilevel security and data compression technique by applyingHuffman coding and bit stuffing algorithms. Authors have implemented and provethe bit stuff and Huffman coding can provide high level security and highperformance on compression processes.

The data compression technique reducesthe transmission time and bandwidth utilization.Conclusion:            Data security and storage reduction tasksare more important in the current trend. The analysis of encoding techniquesand tools for compression is discussed. This paper specifically concentrated onthe Huffman coding related works and its drawbacks. The survey gives thetechnique of existing compression techniques for text data. However, theHuffman coding compression is popular, but the execution issues arises.

Thispaper gives idea about such issues in brief. From these analysis, an optimalsolution can be found.References:1. Sayood, Khalid. Introduction to datacompression. Morgan Kaufmann, 2017.2. Chang,Weiling, Binxing Fang, Xiaochun Yun, and Shupeng Wang.

“The block losslessdata compression algorithm.” International Journal of ComputerScience and Network Security (IJCSNS) 9, no. 10 (2009): 116.3. Sharma, Neha, Jasmeet Kaur, and Navmeet Kaur.”A review on various Lossless text data compressiontechniques.” International Journal of Engineering Sciences, Issue 2(2014).

4. Chau, Savio N., and Ridwan Rashid. “Datacompression with Huffman code on multicore processors.” U.S. Patent9,258,013, issued February 9, 2016.

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

IEEE, 2015.6. Sarkar,Subhra J., Nabendu Kr Sarkar, and Antra Banerjee. “A novel Huffman codingbased 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, andAbd Rahman Ramli. “A New Lossless Method of Huffman Coding for Text DataCompression and Decompression Process with FPGA Implementation.” Journalof Engineering and Applied Sciences 100, no. 3 (2016): 402-407.

8. Kodabagi, M. M., M. V. Jerabandi, and NagarajGadagin. “Multilevel security and compression of text data using bitstuffing and huffman coding.

” In Applied and Theoretical Computingand Communication Technology (iCATccT), 2015 International Conference on,pp. 800-804. IEEE, 2015.


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