Biblio

Secure hash functions are widely used in cryptographic algorithms to secure against diverse attacks. A one-way secure hash function is used in the various research fields to secure, for instance, blockchain. Notably, most of the hash functions provide security based on static parameters and publicly known operations. Consequently, it becomes easier to attack by the attackers because all parameters and operations are predefined. The publicly known parameters and predefined operations make the oracle regenerate the key even though it is a one-way secure hash function. Moreover, the sensitive data is mixed with the predefined constant where an oracle may find a way to discover the key. To address the above issues, we propose a novel one-way secure hash algorithm, OSHA for short, to protect sensitive data against attackers. OSHA depends on a pseudo-random number generator to generate a hash value. Particularly, OSHA mixes multiple pseudo-random numbers to produce a secure hash value. Furthermore, OSHA uses dynamic parameters, which is difficult for adversaries to guess. Unlike conventional secure hash algorithms, OSHA does not depend on fixed constants. It replaces the fixed constant with the pseudo-random numbers. Also, the input message is not mixed with the pseudo-random numbers; hence, there is no way to recover and reverse the process for the adversaries.

The occurrences of accidents in mining industries owing to the fragile health conditions of mine workers are reportedly increasing. Health conditions measured as heart rate or pulse, glycemic index, and blood pressure are often crucial parameters that lead to failure in proper reasoning when not within acceptable ranges. These parameters, such as heartbeat rate can be measured continuously using sensors. The data can be monitored remotely and, when found to be of concern, can send necessary alarms to the mine manager. The early alarm notification enables the mine manager with better preparedness for managing the reach of first aid to the accident spot and thereby reduce mine fatalities drastically. This paper presents a framework for deterring accidents in mines with the help of the Grey Wolf Optimization approach.