<p>Cloud computing has spread widely among different organizations due to its advantages, such as cost reduction, resource pooling, broad network access, and ease of administration. It increases the abilities of physical resources by optimizing shared use. Clients’ valuable items (data and applications) are moved outside of regulatory supervision in a shared environment where many clients are grouped together. However, this process poses security concerns, such as sensitive information theft and personally identifiable data leakage. Many researchers have contributed to reducing the problem of data security in cloud computing by developing a variety of technologies to secure cloud data, including encryption. In this study, a set of encryption algorithms (advance encryption standard (AES), data encryption standard (DES), Blowfish, Rivest-Shamir-Adleman (RSA) encryption, and international data encryption algorithm (IDEA) was compared in terms of security, data encipherment capacity, memory usage, and encipherment time to determine the optimal algorithm for securing cloud information from hackers. Results show that RSA and IDEA are less secure than AES, Blowfish, and DES). The AES algorithm encrypts a huge amount of data, takes the least encipherment time, and is faster than other algorithms, and the Blowfish algorithm requires the least amount of memory space.</p>
At present, security is significant for individuals and organizations. All information need security to prevent theft, leakage, alteration. Security must be guaranteed by applying some or combining cryptography algorithms to the information. Encipherment is the method that changes plaintext to a secure form called cipherment. Encipherment includes diverse types, such as symmetric and asymmetric encipherment. This study proposes an improved version of the advanced encryption standard (AES) algorithm called optimized advanced encryption standard (OAES). The OAES algorithm utilizes sine map and random number to generate a new key to enhance the complexity of the generated key. Thereafter, multiplication operation was performed on the original text, thereby creating a random matrix (4×4) before the five stages of the coding cycles. A random substitution-box (S-Box) was utilized instead of a fixed S-Box. Finally, we utilized the eXclusive OR (XOR) operation with digit 255, also with the key that was generated last. This research compared the features of the AES and OAES algorithms, particularly the extent of complexity, key size, and number of rounds. The OAES algorithm can enhance complexity of encryption and decryption by using random values, random S-Box, and chaotic maps, thereby resulting in difficulty guessing the original text.
There are various ways of social communication including writing (WhatsApp, Messenger, Facebook, Twitter, Skype, etc), calling (mobile phone) and voice recording (record your voice and then send it to the other party), but there are ways to eavesdropping the calls and voice messages, One way to solve this problem is via cryptographic approach. Chaos cryptography build on top of nonlinear dynamics chaotic system has gained some footstep in data security. It provides an alternative to conventional cryptography built on top of mathematical structures. This research focuses on the protection of speech recording by encrypting it with multiple encryption algorithms, including chaotic maps (Logistic Map and Sine Maps).
Voice signal analysis is becoming one of the most significant examination in clinical practice due to the importance of extracting related parameters to reflect the patient's health. In this regard, various acoustic studies have been revealed that the analysis of laryngeal, respiratory and articulatory function may be efficient as an early indicator in the diagnosis of Parkinson disease (PD). PD is a common chronic neurodegenerative disorder, which affects a central nervous system and it is characterized by progressive loss of muscle control. Tremor, movement and speech disorders are the main symptoms of PD. The diagnosis decision of PD is obtained by continued clinical observation which relies on expert human observer. Therefore, an additional diagnosis method is desirable for most comfortable and timely detection of PD as well as faster treatment is needed. In this study, we develop and validate automated classification algorithms, which are based on Naïve Bayes and K-Nearest Neighbors (KNN) using voice signal measurements to predict PD. According to the results, the diagnostic performance provided by the automated classification algorithm using Naïve Bayes was superior to that of the KNN and it is useful as a predictive tool for PD screening with a high degree of accuracy, approximately 93.3%.
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