Research of collision properties of the modified UMAC algorithm on crypto-code constructions

The transfer of information by telecommunication channels is accompanied by message hashing to control the integrity of the data and confirm the authenticity of the data. When using a reliable hash function, it is computationally difficult to create a fake message with a pre-existing hash code, however, due to the weaknesses of specific hashing algorithms, this threat can be feasible. To increase the level of cryptographic strength of transmitted messages over telecommunication channels, there are ways to create hash codes, which, according to practical research, are imperfect in terms of the speed of their formation and the degree of cryptographic strength. The collisional properties of hashing functions formed using the modified UMAC algorithm using the methodology for assessing the universality and strict universality of hash codes are investigated. Based on the results of the research, an assessment of the impact of the proposed modifications at the last stage of the generation of authentication codes on the provision of universal hashing properties was presented. The analysis of the advantages and disadvantages that accompany the formation of the hash code by the previously known methods is carried out. The scheme of cascading generation of data integrity and authenticity control codes using the UMAC algorithm on crypto-code constructions has been improved. Schemes of algorithms for checking hash codes were developed to meet the requirements of universality and strict universality. The calculation and analysis of collision search in the set of generated hash codes was carried out according to the requirements of a universal and strictly universal class for creating hash codes

Everlasting security of quantum key distribution with 1K-DWCDM and quadratic hash

Quantum key distribution (QKD) offers a very strong property called everlasting security, which says if authentication is unbroken during the execution of QKD, the generated key remains information-theoretically secure indefinitely. For this purpose, we propose the use of certain universal hashing based MACs for use in QKD, which are fast, very efficient with key material, and are shown to be highly secure. Universal hash functions are ubiquitous in computer science with many applications ranging from quantum key distribution and information security to data structures and parallel computing. In QKD, they are used at least for authentication, error correction, and privacy amplification. Using results from Cohen [Duke Math. J., 1954], we also construct some new families of $\varepsilon$-almost-$\Delta$-universal hash function families which have much better collision bounds than the well-known Polynomial Hash. Then we propose a general method for converting any such family to an $\varepsilon$-almost-strongly universal hash function family, which makes them useful in a wide range of applications, including authentication in QKD.

Chapter 26. Approximate Model Counting

Model counting, or counting solutions of a set of constraints, is a fundamental problem in Computer Science with diverse applications. Since exact counting is computationally hard (#P complete), approximate counting techniques have received much attention over the past few decades. In this chapter, we focus on counting models of propositional formulas, and discuss in detail universal-hashing based approximate counting, which has emerged as the predominant paradigm for state-of-the-art approximate model counters. These counters are randomized algorithms that exploit properties of universal hash functions to provide rigorous approximation guarantees, while piggybacking on impressive advances in propositional satisfiability solving to scale up to problem instances with a million variables. We elaborate on various choices in designing such approximate counters and the implications of these choices. We also discuss variants of approximate model counting, such as DNF counting and weighted counting.

Secure WiFi-Direct Using Key Exchange for IoT Device-to-Device Communications in a Smart Environment

With the rapid growth of Internet of Things (IoT) devices around the world, thousands of mobile users share many data with each other daily. IoT communication has been developed in the past few years to ensure direct connection among mobile users. However, wireless vulnerabilities exist that cause security concerns for IoT device-to-device (D2D) communication. This has become a serious debate, especially in smart environments where highly sensitive information is exchanged. In this paper, we study the security requirements in IoT D2D communication. In addition, we propose a novel authentication approach called Secure Key Exchange with QR Code (SeKeQ) to verify user identity by ensuring an automatic key comparison and providing a shared secret key using Diffie-Hellman key agreement with an SHA-256 hash. To evaluate the performance of SeKeQ, we ran a testbed using devices with a WiFi-Direct communication interface. The obtained results depict that our proposal can offer the required security functions including key exchange, data confidentiality, and integrity. In addition, our proposal can reach the same security performances as MANA (Manual Authentication) and UMAC (Universal-Hashing Message Authentication Code) but with 10 times fewer key computations and reduced memory occupancy.

GANAK: A Scalable Probabilistic Exact Model Counter

Given a Boolean formula F, the problem of model counting, also referred to as #SAT, seeks to compute the number of solutions of F. Model counting is a fundamental problem with a wide variety of applications ranging from planning, quantified information flow to probabilistic reasoning and the like. The modern #SAT solvers tend to be either based on static decomposition, dynamic decomposition, or a hybrid of the two. Despite dynamic decomposition based #SAT solvers sharing much of their architecture with SAT solvers, the core design and heuristics of dynamic decomposition-based #SAT solvers has remained constant for over a decade. In this paper, we revisit the architecture of the state-of-the-art dynamic decomposition-based #SAT tool, sharpSAT, and demonstrate that by introducing a new notion of probabilistic component caching and the usage of universal hashing for exact model counting along with the development of several new heuristics can lead to significant performance improvement over state-of-the-art model-counters. In particular, we develop GANAK, a new scalable probabilistic exact model counter that outperforms state-of-the-art exact and approximate model counters sharpSAT and ApproxMC3 respectively, both in terms of PAR-2 score and the number of instances solved. Furthermore, in our experiments, the model count returned by GANAK was equal to the exact model count for all the benchmarks. Finally, we observe that recently proposed preprocessing techniques for model counting benefit exact model counters while hurting the performance of approximate model counters.