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.

Merkle tree is applied in diverse applications, namely, Blockchain, smart grid, IoT, Biomedical, financial transactions, etc., to verify authenticity and integrity. Also, the Merkle tree is used in privacy-preserving computing. However, the Merkle tree is a computationally costly data structure. It uses cryptographic string hash functions to partially verify the data integrity and authenticity of a data block. Moreover, the verification process creates unnecessary network traffic because it requires partial hash values to verify a particular block. Furthermore, the performance of the Merkle tree also depends on the network latency. Therefore, it is not feasible for most of the applications. To address the above issue, we proposed an alternative model to replace the Merkle tree, called HEX-BLOOM, and it is implemented using hash, Exclusive-OR, and Bloom Filter. Our proposed model’s performance does not depends on network latency for verification of data block’s authenticity and integrity. HEX-BLOOM uses an approximation model, Bloom Filter. Moreover, it employs a deterministic model for final verification of the correctness. In this paper, we show that our proposed model is better than the state-of-the-art Merkle tree in every aspect.

Symmetric key cryptography is applied in almost all secure communications to protect all sensitive information from attackers, for instance, banking, and thus, it requires extra attention due to diverse applications. Moreover, it is vulnerable to various attacks, for example, cryptanalysis attacks. Cryptanalysis attacks are possible due to a single-keyed encryption system. The state-of-the-art symmetric communication protocol uses a single secret key to encrypt/decrypt the entire communication to exchange data/message that poses security threats. Therefore, in this paper, we present a new secure communication protocol based on Diffie-Hellman cryptographic algorithms, called Stealth. It is a symmetric-key cryptographic protocol to enhance the security of modern communication with truly random numbers. Additionally, it applies a pseudo-random number generator. Initially, Stealth uses the Diffie-Hellman algorithm to compute four shared secret keys. These shared secret keys are used to generate four different private keys to encrypt for the first block of the message for symmetric communication. Stealth changes its private keys in each communication, making it very hard to break the security protocol. Moreover, the four shared secret keys create additional complexity for the adversary to overcome, and hence, it can provide highly tight security in communications. Stealth neither replaces the existing protocol nor authentication mechanism, but it creates another security layer to the existing protocol to ensure the security measurement’s tightness.

Data secrecy is a major concern in many domains. Nowadays, the data are kept in tight security with high privacy. Users do not want to share their secret information with anyone; however, the users' confidential data are not protected from the administrators. Administrators can read the users' data. Why should any Administrator read users' data? To address this issue, we propose a new secrecy protocol to store data secretly, named Secret Cloud Storage, SecretStore for short, to enable Secrecy as a Service model over the Cloud Computing paradigm. This paper demonstrates how to protect users' data from any unintended users, including the data administrators. Moreover, we introduce tight security using the client-side symmetric cryptography method. In addition, we devise a forgetful private key to generate or regenerate a private key to encrypt or decrypt based on a secret word. We also show how to strengthen the weak password. Finally, we demonstrate how to implement the Secrecy as a Service model in Cloud Storage using highly unpredictable private keys.

Bloom Filter is a probabilistic data structure for the membership query, and it has been intensely experimented in various fields to reduce memory consumption and enhance a system's performance. Bloom Filter is classified into two key categories: counting Bloom Filter (CBF), and non-counting Bloom Filter. CBF has a higher false positive probability than standard Bloom Filter (SBF), i.e., CBF uses a higher memory footprint than SBF. But CBF can address the issue of the false negative probability. Notably, SBF is also false negative free, but it cannot support delete operations like CBF. To address these issues, we present a novel counting Bloom Filter based on SBF and 2D Bloom Filter, called countBF. countBF uses a modified murmur hash function to enhance its various requirements, which is experimentally evaluated. Our experimental results show that countBF uses 1.96× and 7.85× less memory than SBF and CBF respectively, while preserving lower false positive probability and execution time than both SBF and CBF. The overall accuracy of countBF is 99.999921, and it proves the superiority of countBF over SBF and CBF. Also, we compare with other state-of-the-art counting Bloom Filters.

Designing and developing a true random number generator is a grand challenge for all time. It is highly necessitated in cryptography and various domains, such as simulation and other scientific applications. Therefore, various random number generators are emerging. Many true random number algorithms have already been proposed, which are very complex and require specific devices; for instance, a quantum random number generator depends on quantum devices. Moreover, diverse true random number generators are devised based on light, image, voltage, currents, etc., to achieve true randomness. Thus, it is a challenging task to generate a truly random number in any computing device. Therefore, we propose a general-purpose true random number generator for a conventional computing device, called Rando. The time complexity of Rando is$O(m)$, where$m$is the bit size of a random number, but it does not use extra spaces. Our proposed algorithm is a simple and straightforward true random number generator based on hashing techniques and system clocks yet powerful. Our experimental results show that Rando outperforms state-of-the-art techniques in randomness. Rando is validated its true randomness in NIST SP 800–22 and passes all 15 statistical tests of randomness.

In this Exa byte scale era, data increases at an exponential rate. This is in turn generating a massive amount of metadata in the file system. Hadoop is the most widely used framework to deal with big data. Due to this growth of huge amount of metadata, however, the efficiency of Hadoop is questioned numerous times by many researchers. Therefore, it is essential to create an efficient and scalable metadata management for Hadoop. Hash-based mapping and subtree partitioning are suitable in distributed metadata management schemes. Subtree partitioning does not uniformly distribute workload among the metadata servers, and metadata needs to be migrated to keep the load roughly balanced. Hash-based mapping suffers from a constraint on the locality of metadata, though it uniformly distributes the load among NameNodes, which are the metadata servers of Hadoop. In this paper, we present a circular metadata management mechanism named dynamic circular metadata splitting (DCMS). DCMS preserves metadata locality using consistent hashing and locality-preserving hashing, keeps replicated metadata for excellent reliability, and dynamically distributes metadata among the NameNodes to keep load balancing. NameNode is a centralized heart of the Hadoop. Keeping the directory tree of all files, failure of which causes the single point of failure (SPOF). DCMS removes Hadoop’s SPOF and provides an efficient and scalable metadata management. The new framework is named ‘Dr. Hadoop’ after the name of the authors.