Hash Function Generation Research Articles

DNA TRIKKY Based Security Mechanism for Radio Frequency Identification Protocol

Radio frequency identification (RFID) technology has seen rapid expansion in recent years, used in a variety of applications like tracking, monitoring, ticketing, supply-chain management, contactless payments, etc. Several researchers have recommended elliptical curve cryptography (ECC) as a great cryptosystem to pair with RFID technology to increase security and privacy. However, RFID systems have major problems with insecure communication routes, which pose security concerns. In this research, a DNA TRIKKY based security mechanism has been proposed for RFID protocol to mutually authenticate the RFID tag and reader through insecure channels. The proposed solution uses a sponge structure and DNA-XOR to enhance the hash function generation process, preventing sensitive data from being leaked from the backend server. Informal security evaluations provide proof that the proposed method can withstand many attacks. According to performance evaluation, the proposed method performs better than the current protocols, making it ideal for use with RFID systems since it necessitates less processing time, communication costs, and storage costs particularly for the tag. According to the results, the proposed authentication paradigm works well with RFID tags that have limited resources. A comparison of the proposed protocol to other protocols shows that it performs more securely. Studies have found that this method outperforms other existing strategies.

An efficient and secure data sharing scheme for cloud data using hash based quadraplet wavelet permuted cryptography approach

SummaryEnsuring the reliable and secure data transmission in cloud systems is very essential and demanding task in the recent decades. For that purpose, various security frameworks have been deployed in the existing works, which are mainly focusing the improving the privacy and confidentiality of cloud data against the unauthenticated users. Yet, it facing the major problems of inefficient accessing, increased time consumption, computational complexity, storage complexity, and memory utilization rate. Hence, this research work intends to develop an advanced and efficient cryptography model, named as, hash based quadraplet wavelet permuted cryptography (HQWPC) for the secured cloud data storage and retrieval operations. Here, the bilinear mapping and group hash function generation processes are performed to generate the keys used for cryptographic operations. Also, the zig‐zag scanning, wavelet coefficients extraction, and permutation processes are accomplished for data encryption. Consequently, the inverse of these operations is performed while decrypting the data. In addition to that, an integrated signature control policy authentication mechanism is employed for validating the authenticity of the cloud data users. This kind of signature verification process could efficiently increase the security level of cloud data. For validating the performance of the proposed security framework, various evaluation metrics have been utilized during analysis. Then, the obtained results are compared with the recent state‐of‐the‐art models for proving the efficiency of the proposed technique over the other techniques.

Performance Evaluation of SHA-3 Final Round Candidate Algorithms on ARM Cortex–M4 Processor

SHA-3 was an open competition initiated by NIST to design new generation of hash functions. This competition was a necessity to overcome the challenges imposed by multiple attacks on MDx family of hash functions including SHA-0 and SHA-1. For this competition, NIST announced a reference platform which did not cover Embedded and Mobile machines. This paper compares the performance of SHA-3 final round candidate algorithms on ARM Cortex-M4 processor (embedded processor) and presents the results. Cycles per Byte is used as performance metric. Cortex-M4 based Stellaris® LM4F232 Evaluation Board (EK-LM4F232) from Texas Instruments is used for performance evaluation.

Large-Scale Unsupervised Hashing with Shared Structure Learning.

Hashing methods are effective in generating compact binary signatures for images and videos. This paper addresses an important open issue in the literature, i.e., how to learn compact hash codes by enhancing the complementarity among different hash functions. Most of prior studies solve this problem either by adopting time-consuming sequential learning algorithms or by generating the hash functions which are subject to some deliberately-designed constraints (e.g., enforcing hash functions orthogonal to one another). We analyze the drawbacks of past works and propose a new solution to this problem. Our idea is to decompose the feature space into a subspace shared by all hash functions and its complementary subspace. On one hand, the shared subspace, corresponding to the common structure across different hash functions, conveys most relevant information for the hashing task. Similar to data de-noising, irrelevant information is explicitly suppressed during hash function generation. On the other hand, in case that the complementary subspace also contains useful information for specific hash functions, the final form of our proposed hashing scheme is a compromise between these two kinds of subspaces. To make hash functions not only preserve the local neighborhood structure but also capture the global cluster distribution of the whole data, an objective function incorporating spectral embedding loss, binary quantization loss, and shared subspace contribution is introduced to guide the hash function learning. We propose an efficient alternating optimization method to simultaneously learn both the shared structure and the hash functions. Experimental results on three well-known benchmarks CIFAR-10, NUS-WIDE, and a-TRECVID demonstrate that our approach significantly outperforms state-of-the-art hashing methods.

Hash function generation by means of Gene Expression Programming

: Cryptographic hash functions are fundamental primitives in modern cryptography and have many security applications (data integrity checking, cryptographic protocols, digital signatures, pseudo random number generators etc.). At the same time novel hash functions are designed (for instance in the framework of the SHA-3 contest organized by the National Institute of Standards and Technology (NIST)), the cryptanalysts exhibit a set of statistical metrics (propagation criterion, frequency analysis etc.) able to assert the quality of new proposals. Also, rules to design good hash functions are now known and are followed in every reasonable proposal of a new hash scheme. This article investigates the ways to build on this experiment and those metrics to generate automatically compression functions by means of Evolutionary Algorithms (EAs). Such functions are at the heart of the construction of iterative hash schemes and it is therefore crucial for them to hold good properties. Actually, the idea to use nature-inspired heuristics for the design of such cryptographic primitives is not new: this approach has been successfully applied in several previous works, typically using the Genetic Programming (GP) heuristic [1]. Here, we exploit a hybrid meta-heuristic for the evolutionary process called Gene Expression Programming (GEP) [2] that appeared far more efficient computationally speaking compared to the GP paradigm used in the previous papers. In this context, the GEPHashSearch framework is presented. As it is still a work in progress, this article focuses on the design aspects of this framework (individuals definitions, fitness objectives etc.) rather than on complete implementation details and validation results. Note that we propose to tackle the generation of compression functions as a multi-objective optimization problem in order to identify the Pareto front i.e. the set of non-dominated functions over the four fitness criteria considered. If this goal is not yet reached, the first experimental results in a mono-objective context are promising and open the perspective of fruitful contributions to the cryptographic community.

Hash function generation by means of Gene Expression Programming

: Cryptographic hash functions are fundamental primitives in modern cryptography and have many security applications (data integrity checking, cryptographic protocols, digital signatures, pseudo random number generators etc.). At the same time novel hash functions are designed (for instance in the framework of the SHA-3 contest organized by the National Institute of Standards and Technology (NIST)), the cryptanalysts exhibit a set of statistical metrics (propagation criterion, frequency analysis etc.) able to assert the quality of new proposals. Also, rules to design good hash functions are now known and are followed in every reasonable proposal of a new hash scheme. This article investigates the ways to build on this experiment and those metrics to generate automatically compression functions by means of Evolutionary Algorithms (EAs). Such functions are at the heart of the construction of iterative hash schemes and it is therefore crucial for them to hold good properties. Actually, the idea to use nature-inspired heuristics for the design of such cryptographic primitives is not new: this approach has been successfully applied in several previous works, typically using the Genetic Programming (GP) heuristic [1]. Here, we exploit a hybrid meta-heuristic for the evolutionary process called Gene Expression Programming (GEP) [2] that appeared far more efficient computationally speaking compared to the GP paradigm used in the previous papers. In this context, the GEPHashSearch framework is presented. As it is still a work in progress, this article focuses on the design aspects of this framework (individuals definitions, fitness objectives etc.) rather than on complete implementation details and validation results. Note that we propose to tackle the generation of compression functions as a multi-objective optimization problem in order to identify the Pareto front i.e. the set of non-dominated functions over the four fitness criteria considered. If this goal is not yet reached, the first experimental results in a mono-objective context are promising and open the perspective of fruitful contributions to the cryptographic community.

Murasaki: A Fast, Parallelizable Algorithm to Find Anchors from Multiple Genomes

BackgroundWith the number of available genome sequences increasing rapidly, the magnitude of sequence data required for multiple-genome analyses is a challenging problem. When large-scale rearrangements break the collinearity of gene orders among genomes, genome comparison algorithms must first identify sets of short well-conserved sequences present in each genome, termed anchors. Previously, anchor identification among multiple genomes has been achieved using pairwise alignment tools like BLASTZ through progressive alignment tools like TBA, but the computational requirements for sequence comparisons of multiple genomes quickly becomes a limiting factor as the number and scale of genomes grows.Methodology/Principal FindingsOur algorithm, named Murasaki, makes it possible to identify anchors within multiple large sequences on the scale of several hundred megabases in few minutes using a single CPU. Two advanced features of Murasaki are (1) adaptive hash function generation, which enables efficient use of arbitrary mismatch patterns (spaced seeds) and therefore the comparison of multiple mammalian genomes in a practical amount of computation time, and (2) parallelizable execution that decreases the required wall-clock and CPU times. Murasaki can perform a sensitive anchoring of eight mammalian genomes (human, chimp, rhesus, orangutan, mouse, rat, dog, and cow) in 21 hours CPU time (42 minutes wall time). This is the first single-pass in-core anchoring of multiple mammalian genomes. We evaluated Murasaki by comparing it with the genome alignment programs BLASTZ and TBA. We show that Murasaki can anchor multiple genomes in near linear time, compared to the quadratic time requirements of BLASTZ and TBA, while improving overall accuracy.Conclusions/SignificanceMurasaki provides an open source platform to take advantage of long patterns, cluster computing, and novel hash algorithms to produce accurate anchors across multiple genomes with computational efficiency significantly greater than existing methods. Murasaki is available under GPL at http://murasaki.sourceforge.net.

Architecture and Compiler Optimizations for Data Bandwidth Improvement in Configurable Processors

Many commercially available embedded processors are capable of extending their base instruction set for a specific domain of applications. While steady progress has been made in the tools and methodologies of automatic instruction set extension for configurable processors, the limited data bandwidth available in the core processor (e.g., the number of simultaneous accesses to the register file) becomes a potential performance bottleneck. In this paper, we first present a quantitative analysis of the data bandwidth limitation in configurable processors, and then propose a novel low-cost architectural extension and associated compilation techniques to address the problem. Specifically, we embed a single control bit in the instruction op-codes to selectively copy the execution results to a set of hash-mapped shadow registers in the write-back stage. This can efficiently reduce the communication overhead due to data transfers between the core processor and the custom logic. We also present a novel simultaneous global shadow register binding with a hash function generation algorithm to take full advantage of the extension. The application of our approach leads to a nearly optimal performance speedup