Omid Torki; Maede Ashouri-Talouki; Mojtaba Mahdavi
Abstract
Steganography is a solution for covert communication and blockchain is a p2p network for data transmission, so the benefits of blockchain can be used in steganography. In this paper, we discuss the advantages of blockchain in steganography, which include the ability to embed hidden data without manual ...
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Steganography is a solution for covert communication and blockchain is a p2p network for data transmission, so the benefits of blockchain can be used in steganography. In this paper, we discuss the advantages of blockchain in steganography, which include the ability to embed hidden data without manual change in the original data, as well as the readiness of the blockchain platform for data transmission and storage. By reviewing the previous four steganography schemes in blockchain, we have examined their drawback and shown that most of them are non-practical schemes for steganography in blockchain. We have proposed two algorithms for steganography in blockchain, the first one is a high-capacity algorithm for the key and the steganography algorithm exchange and switching, and the second one is a medium-capacity algorithm for embedding hidden data. The proposed method is a general method for steganography in each blockchain, and we investigate how it can be implemented in two most popular blockchains, Bitcoin and Ethereum. Experimental result shows the efficiency and practicality of proposed method in terms of execution time, latency and steganography fee. Finally, we have explained the challenges of steganography in blockchain from the steganographers' and steganalyzers' point of view.
Saad Ali Alahmari
Abstract
The increasing volatility in pricing and growing potential for profit in digital currency have made predicting the price of cryptocurrency a very attractive research topic. Several studies have already been conducted using various machine-learning models to predict crypto currency prices. ...
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The increasing volatility in pricing and growing potential for profit in digital currency have made predicting the price of cryptocurrency a very attractive research topic. Several studies have already been conducted using various machine-learning models to predict crypto currency prices. This study presented in this paper applied a classic Autoregressive Integrated Moving Average(ARIMA) model to predict the prices of the three major cryptocurrencies âAT Bitcoin, XRP and Ethereum âAT using daily, weekly and monthly time series. The results demonstrated that ARIMA outperforms most other methods in predicting cryptocurrency prices on a daily time series basis in terms of mean absolute error (MAE), mean squared error (MSE) and root mean squared error(RMSE).