Combination Of Modified Least Significant Bit Pseudo Random Number Generator Algorithm With Rice Coding Algorithm For Security And File Compression
In today's digital age, data security is a top priority. The theft of data by unauthorized parties can occur at any time, making it essential to have an effective solution to maintain the confidentiality of information. One method that can be used is Steganography, which in this study applies a modified Least Significant Bit Pseudo Random Number Generator algorithm. Although this method is effective in hiding data, the steganography process often produces larger file sizes, making file delivery slower. Therefore, the merger with the Rice Code compression method is very important to reduce the file size and speed up the shipping process.
The Importance of Data Security in the Digital Era
Data security is a critical issue in the digital era. With the increasing amount of data being generated and shared, the risk of data theft and unauthorized access is higher than ever. This is where Steganography comes in, a method of hiding data within other data, making it difficult for unauthorized parties to detect. The modified Least Significant Bit Pseudo Random Number Generator algorithm is a type of Steganography that works by changing the last bits of storage media data to store new information without disturbing the quality of the media.
The Process of Using an Algorithm
This study consists of two main stages, namely embedding-compression and decompression-extraction. At the embedding stage, the data to be hidden is entered into the file using the modified Least Significant Bit (MLSB) modified algorithm. This algorithm works by changing the last bits of storage media data to store new information without disturbing the quality of the media.
After the hiding process is complete, the resulting file usually has a larger size. This is where the Rice Coding algorithm plays a role. This algorithm is an efficient compression method that can significantly reduce file size. In this stage, some important parameters are used to measure the effectiveness of compression, such as ratio of compression, compression ratio, space savings, and bitrates.
After the file is compressed, the decompression-extraction stage is carried out to take back the hidden message. The parameters used to assess the quality of the steganographic process include the Mean Squared Error (MSE), Peak Signal to Noise Ratio (PSNR), and Running Time.
Research Methodology
The research methodology used in this study involves the following steps:
- Data Collection: The data to be hidden is collected and prepared for the embedding process.
- Embedding: The data is embedded into the file using the modified Least Significant Bit Pseudo Random Number Generator algorithm.
- Compression: The resulting file is compressed using the Rice Coding algorithm.
- Decompression-Extraction: The compressed file is decompressed and the hidden message is extracted.
Research Result
The results of the study show that the combination of modified Least Significant Bit Pseudo Random Number Generator algorithm with Rice Code compression algorithm provides effective solutions in terms of security and reduction of file size. This method is not only able to protect data from theft but also allows the return of messages that have been hidden with quality that is maintained.
The use of this algorithm offers two large advantages: first, securing data from the unauthorized party, and second, speeding up the file shipping process by reducing the size that must be sent. This is very relevant in the world which is increasingly dependent on speed and efficiency in sending information.
Conclusion
The combination of Modified Least Significant Bit Pseudo Random Number Generator Steganography Algorithm and Rice Code Compression Algorithm shows extraordinary potential in increasing data security and file shipping efficiency. Through this research, it is expected to contribute to the field of data security and file compression, as well as open up opportunities for further research in the future. Further development of this method can be a long-term solution in dealing with data security threats in the growing digital era.
Future Research Directions
The following are some potential future research directions:
- Improving the Efficiency of the Algorithm: Further research can be conducted to improve the efficiency of the algorithm, making it faster and more effective.
- Applying the Algorithm to Other Fields: The algorithm can be applied to other fields, such as image and video compression, to improve the efficiency of data transmission.
- Developing a More Secure Algorithm: Further research can be conducted to develop a more secure algorithm, making it more difficult for unauthorized parties to detect and access the hidden data.
Limitations of the Study
The following are some limitations of the study:
- Limited Data Set: The study was conducted using a limited data set, which may not be representative of all types of data.
- Limited Algorithm Variations: The study only used a limited variation of the algorithm, which may not be representative of all possible variations.
- Limited Testing: The study only tested the algorithm using a limited number of tests, which may not be representative of all possible testing scenarios.
Recommendations for Future Research
Based on the results of this study, the following are some recommendations for future research:
- Conduct Further Research on the Algorithm: Further research can be conducted to improve the efficiency and security of the algorithm.
- Apply the Algorithm to Other Fields: The algorithm can be applied to other fields, such as image and video compression, to improve the efficiency of data transmission.
- Develop a More Secure Algorithm: Further research can be conducted to develop a more secure algorithm, making it more difficult for unauthorized parties to detect and access the hidden data.
Q&A: Combination of Modified Least Significant Bit Pseudo Random Number Generator Algorithm with Rice Coding Algorithm for Security and File Compression =====================================================
In this article, we will answer some frequently asked questions about the combination of modified Least Significant Bit Pseudo Random Number Generator algorithm with Rice Coding algorithm for security and file compression.
Q: What is the main purpose of this study?
A: The main purpose of this study is to investigate the effectiveness of combining modified Least Significant Bit Pseudo Random Number Generator algorithm with Rice Coding algorithm for security and file compression.
Q: What is the modified Least Significant Bit Pseudo Random Number Generator algorithm?
A: The modified Least Significant Bit Pseudo Random Number Generator algorithm is a type of Steganography that works by changing the last bits of storage media data to store new information without disturbing the quality of the media.
Q: What is the Rice Coding algorithm?
A: The Rice Coding algorithm is an efficient compression method that can significantly reduce file size. It is used to compress the resulting file after the hiding process is complete.
Q: What are the advantages of using this algorithm?
A: The use of this algorithm offers two large advantages: first, securing data from the unauthorized party, and second, speeding up the file shipping process by reducing the size that must be sent.
Q: How does the algorithm work?
A: The algorithm works by embedding the data to be hidden into the file using the modified Least Significant Bit Pseudo Random Number Generator algorithm, and then compressing the resulting file using the Rice Coding algorithm.
Q: What are the parameters used to measure the effectiveness of compression?
A: The parameters used to measure the effectiveness of compression include ratio of compression, compression ratio, space savings, and bitrates.
Q: What are the parameters used to assess the quality of the steganographic process?
A: The parameters used to assess the quality of the steganographic process include the Mean Squared Error (MSE), Peak Signal to Noise Ratio (PSNR), and Running Time.
Q: What are the limitations of the study?
A: The limitations of the study include limited data set, limited algorithm variations, and limited testing.
Q: What are the recommendations for future research?
A: The recommendations for future research include conducting further research on the algorithm, applying the algorithm to other fields, and developing a more secure algorithm.
Q: What are the potential applications of this algorithm?
A: The potential applications of this algorithm include image and video compression, data transmission, and secure communication.
Q: How can this algorithm be used in real-world scenarios?
A: This algorithm can be used in real-world scenarios such as secure data transmission, image and video compression, and secure communication.
Q: What are the potential benefits of using this algorithm?
A: The potential benefits of using this algorithm include improved data security, reduced file size, and increased efficiency in data transmission.
Q: What are the potential challenges of using this algorithm?
A: The potential challenges of using this algorithm include limited data set, limited algorithm variations, and limited testing.
Q: How can the algorithm be improved?
A: The algorithm can be improved by conducting further research on the algorithm, applying the algorithm to other fields, and developing a more secure algorithm.
Q: What are the potential future directions of this research?
A: The potential future directions of this research include improving the efficiency of the algorithm, applying the algorithm to other fields, and developing a more secure algorithm.
Q: How can this research contribute to the field of data security and file compression?
A: This research can contribute to the field of data security and file compression by providing a new and effective method for securing data and reducing file size.
Q: What are the potential implications of this research?
A: The potential implications of this research include improved data security, reduced file size, and increased efficiency in data transmission.
Q: How can this research be applied in real-world scenarios?
A: This research can be applied in real-world scenarios such as secure data transmission, image and video compression, and secure communication.
Q: What are the potential benefits of applying this research in real-world scenarios?
A: The potential benefits of applying this research in real-world scenarios include improved data security, reduced file size, and increased efficiency in data transmission.