Private and Secure Post-quantum Verifiable Random Function with NIZK Proof and Ring-LWE Encryption in Blockchain

In this work, we introduce the first practical post-quantum verifiable random function (VRF) that relies on well-known (module) lattice problems, namely Module-SIS and Module-LWE. Our construction, named LB-VRF, results in a VRF value of only 84 bytes and a proof of around only 5 KB (in comparison to several MBs in earlier works), and runs in about 3 ms for evaluation and about 1 ms for verification.In order to design a practical scheme, we need to restrict the number of VRF outputs per key pair, which makes our construction few-time. Despite this restriction, we show how our few-time LB-VRF can be used in practice and, in particular, we estimate the performance of Algorand using LB-VRF. We find that, due to the significant increase in the communication size in comparison to classical constructions, which is inherent in all existing lattice-based schemes, the throughput in LB-VRF-based consensus protocol is reduced, but remains practical. In particular, in a medium-sized network with 100 nodes, our platform records a 1.14\(\times \) to 3.4\(\times \) reduction in throughput, depending on the accompanying signature used. In the case of a large network with 500 nodes, we can still maintain at least 24 transactions per second. This is still much better than Bitcoin, which processes only about 5 transactions per second.KeywordsPost-quantumVerifiable random functionBlockchainLatticeAlgorand

This paper addresses a modified dynamic programming parallel algorithm, and its associated architecture of temporal and spatial complexity of O(N), with N number of processing elements involved in computation. The modification consists of the algorithm adaptation to image interest point matching and architecture optimization. The parallel algorithm simulates the designed architecture, validates it functionally, and allows temporal performance prediction. The proposed algorithm of low spatial and temporal complexities is very suitable for real time on board applications (autonomous robot guidance, trajectory matching, blind vision guidance...).

Mixed-protocol multi-party computation framework towards complex computation tasks with malicious security

This review chapter provides an in-depth exploration of various aspects of data security, ranging from foundational principles to advanced techniques. It sheds light on the importance of maintaining confidentiality, integrity, and availability of data and presents a comprehensive overview of technologies and methodologies that can be employed to achieve robust data security. In today's digital age, ensuring the security and protection of data has become paramount. This research chapter explores various topics related to data security, including confidentiality, integrity, and availability. It also delves into the role of blockchain technology, zero-trust architecture, secure coding practices, legal and ethical considerations, homomorphic encryption, secure multi-party computation, hash functions and digital signatures, secure communication protocols, cryptocurrency and blockchain security, as well as cryptanalysis and side-channel attacks.It highlights various techniques and technologies that can be employed to protect data confidentiality, including encryption and access control mechanisms. Blockchain technology has emerged as a groundbreaking solution for enhancing data security. Zero-trust architecture challenges traditional security models by adopting a holistic and dynamic approach to data protection. Secure coding practices and DevSecOps play a vital role in developing secure software applications. The chapter investigates the importance of integrating security measures throughout the software development lifecycle. It emphasizes the adoption of secure coding practices and the implementation of DevSecOps methodologies to build resilient and secure software systems. Legal and ethical considerations are crucial when addressing data security. Homomorphic encryption and secure multi-party computation are advanced cryptographic techniques that allow data to be processed securely while encrypted. The chapter examines the principles and applications of these techniques, highlighting their significance in scenarios where privacy is paramount. Hash functions and digital signatures form the backbone of data authentication and integrity. The chapter discusses the properties and applications of hash functions and digital signatures in verifying the integrity and authenticity of data. Secure communication protocols are essential for protecting data during transmission. The chapter explores various secure communication protocols, such as Transport Layer Security (TLS), and examines their role in ensuring data confidentiality, integrity, and authenticity. Cryptocurrency and blockchain security focus on the secure storage and transfer of digital assets. The research chapter investigates the security considerations specific to cryptocurrencies and explores how block chain technology can mitigate vulnerabilities and ensure the trustworthiness of transactions. Cryptanalysis and side-channel attacks are threats to cryptographic systems. The chapter discusses these attack techniques, highlighting their potential risks and the measures that can be taken to counter them.

NIST SP800-22 is a statistical test suite for determining whether given sequences are random or not for each statistical test. The statistical test suite has been used widely. However, it was not mentioned in the statistical test suite how many the ratio of passing all the 15 kinds of tests should be for the target generator to be regarded as the ideally true random number generator. In this paper, the ratio of passing all the 15 kinds of tests for the ideally true random number sequences is derived by the theoretical analysis. To verify the theoretical deduction, the statistical tests have been performed on several well known random number generators, such as AES, 3DES, SHA1, stream ciphers wined in eStream project, and some perfect pseudo-random number generator recommended by NIST. The result of the numerical simulations is accord with the theoretical analysis.

Secure multi-party computation (SMC) is a cryptographic protocol that allows participants to compute the desired output without revealing their inputs. A variety of results related to increasing the efficiency of SMC protocol have been reported, and thus, SMC can be used in various applications. With the SMC protocol in smart grids, it becomes possible to obtain information for load balancing and various statistics, without revealing sensitive user information. To prevent malicious users from tampering with input values, SMC requires cheater detection. Several studies have been conducted on SMC with cheater detection, but none of these has been able to guarantee the fairness of the protocol. In such cases, only a malicious user can obtain a correct output prior to detection. This can be a critical problem if the result of the computation is real-time information of considerable economic value. In this paper, we propose a fair and secure multi-party computation protocol, which detects malicious parties participating in the protocol before computing the final output and prevents them from obtaining it. The security of our protocol is proven in the universal composability framework. Furthermore, we develop an enhanced version of the protocol that is more efficient when computing an average after detecting cheaters. We apply the proposed protocols to a smart grid as an application and analyze their efficiency in terms of computational cost.

Calcium signals have been implicated in the regulation of many diverse cellular processes. The problem of how information from extracellular signals is delivered with specificity and fidelity using fluctuations in cytosolic Ca2+ concentration remains unresolved. The capacity of cells to generate Ca2+ signals of sufficient spatial and temporal complexity is the primary constraint on their ability to effectively encode information through Ca2+. Over the past decade, a large body of literature has dealt with some basic features of Ca2+-handling in cells, as well as the multiplicity and functional diversity of intracellular Ca2+ stores and extracellular Ca2+ influx pathways. In principle, physiologists now have the necessary information to attack the problem of function- and agonist-specificity in Ca2+ signal transduction. This review explores the data indicating that Ca2+ release from diverse sources, including many types of intracellular stores, generates Ca2+ signals with sufficient complexity to regulate the vast number of cellular functions that have been reported as Ca2+-dependent. Some examples where such complexity may relate to neuroendocrine regulation of hormone secretion/synthesis are discussed. We show that the functional and spatial heterogeneity of Ca2+ stores generates Ca2+ signals with sufficient spatiotemporal complexity to simultaneously control multiple Ca2+-dependent cellular functions in neuroendocrine systems.

Calcium signals have been implicated in the regulation of many diverse cellular processes. The problem of how information from extracellular signals is delivered with specificity and fidelity using fluctuations in cytosolic Ca2+ concentration remains unresolved. The capacity of cells to generate Ca2+ signals of sufficient spatial and temporal complexity is the primary constraint on their ability to effectively encode information through Ca2+. Over the past decade, a large body of literature has dealt with some basic features of Ca2+-handling in cells, as well as the multiplicity and functional diversity of intracellular Ca2+ stores and extracellular Ca2+ influx pathways. In principle, physiologists now have the necessary information to attack the problem of function- and agonist-specificity in Ca2+ signal transduction. This review explores the data indicating that Ca2+ release from diverse sources, including many types of intracellular stores, generates Ca2+ signals with sufficient complexity to regulate the vast number of cellular functions that have been reported as Ca2+-dependent. Some examples where such complexity may relate to neuroendocrine regulation of hormone secretion/synthesis are discussed. We show that the functional and spatial heterogeneity of Ca2+ stores generates Ca2+ signals with sufficient spatiotemporal complexity to simultaneously control multiple Ca2+-dependent cellular functions in neuroendocrine systems.

Since the definition of attribute reduction based on classic discernibility matrix is different from the definition of attribute reduction based on positive region, simplified discernibility matrix and the corresponding definition of attribute reduction are proposed. At the same time, it is proved that the proposed definition of attribute reduction is identical to the definition of attribution reduction based on positive region. For computing simplified discernibility matrix, IND(C) should usually be calculated at first, so a new algorithm for computing IND(C) is designed, whose temporal complexity is cut down to O(|C||U|). Furthermore, an efficient attribute reduction algorithm is proposed, whose temporal complexity and spatial complexity are cut down to \(\max(O(|C|^2|U'_{pos}||U'|, O(|U||C|))\) and max (O(|C||U′ pos ||U′|, O(|U|)) respectively. At last, an example is used to illustrate the efficiency of the new algorithms.KeywordsRough setdiscernibility matrixsimplified discernibility matrixattribute reductioncomplexity

Secure Multi-party Computation (SMC) protocols enable two or more parties to compute collaboratively generic functions while keeping secret their inputs, sharing only the final result. To achieve this goal, a technique relying on the design of Garbled Circuits (GC) has been firstly proposed by Yao. Garbled circuits are Boolean circuits that can be evaluated using a distributed protocol for computing the result for each gate, till computing the output values. To improve the efficiency of this technique and exploit SMC protocols in practical applications, such as computation outsourcing in untrusted environments, a number of optimizations have been introduced. In this paper we analyze the deployment of Multiple Valued Logic techniques for the design of GC, discussing their impact on the overall computation and communication costs.

A novel approach to the study of spatio-temporal brain dynamics using change-based complexity

Secure Multiparty Computation (SMC) enables multiple parties to cooperate securely without compromising their privacy. SMC has the potential to offer solutions for privacy obstacles in vehicular networks. However, classical SMC implementations suffer from efficiency and security challenges. To address this problem, two quantum communication technologies, Quantum Key Distribution (QKD) and Quantum Oblivious Key Distribution were utilised. These technologies supply symmetric and oblivious keys respectively, allowing fast and secure inter‐vehicular communications. These quantum technologies are integrated with the Faster Malicious Arithmetic Secure Computation with Oblivious Transfer (MASCOT) protocol to form a Quantum Secure Multiparty Computation (QSMC) platform. A lane change service is implemented in which vehicles broadcast private information about their intention to exit the highway. The proposed QSMC approach provides unconditional security even against quantum computer attacks. Moreover, the communication cost of the quantum approach for the lane change use case has decreased by 97% when compared to the classical implementation. However, the computation cost has increased by 42%. For open space scenarios, the reduction in communication cost is especially important, because it conserves bandwidth in the free‐space radio channel, outweighing the increase in computation cost.

A Certificate Authority (CA)-based cryptographic solution for HIPAA privacy/security regulations

Private record linkage (PRL) is the problem of identifying pairs of records that are similar as per an input matching rule from databases held by two parties that do not trust one another. We identify three key desiderata that a PRL solution must ensure: (1) perfect precision and high recall of matching pairs, (2) a proof of end-to-end privacy, and (3) communication and computational costs that scale subquadratically in the number of input records. We show that all of the existing solutions for PRL? including secure 2-party computation (S2PC), and their variants that use non-private or differentially private (DP) blocking to ensure subquadratic cost -- violate at least one of the three desiderata. In particular, S2PC techniques guarantee end-to-end privacy but have either low recall or quadratic cost. In contrast, no end-to-end privacy guarantee has been formalized for solutions that achieve subquadratic cost. This is true even for solutions that compose DP and S2PC: DP does not permit the release of any exact information about the databases, while S2PC algorithms for PRL allow the release of matching records. In light of this deficiency, we propose a novel privacy model, called output constrained differential privacy, that shares the strong privacy protection of DP, but allows for the truthful release of the output of a certain function applied to the data. We apply this to PRL, and show that protocols satisfying this privacy model permit the disclosure of the true matching records, but their execution is insensitive to the presence or absence of a single non-matching record. We find that prior work that combine DP and S2PC techniques even fail to satisfy this end-to-end privacy model. Hence, we develop novel protocols that provably achieve this end-to-end privacy guarantee, together with the other two desiderata of PRL. Our empirical evaluation also shows that our protocols obtain high recall, scale near linearly in the size of the input databases and the output set of matching pairs, and have communication and computational costs that are at least 2 orders of magnitude smaller than S2PC baselines.

In recent years Secure Multiparty Computation (MPC) via Secret Sharing has emerged as a key intersection between the fields of cryptography and machine learning. MPC's ability to allow N-number of participants to hide their inputs while able to share their outputs has presented itself as a solution for creating machine learning models for datasets with sensitive information such as those used in healthcare. Prior to or without MPC creating these models required that the information in these datasets would have to be shared with all participants in cleartext which then could be stolen or misused by dishonest participants or even in transit between the parties. As the demand for answers from machine learning models grows so does the need for privacy and confidentiality for data in those models. But MPC does have some pitfalls with both an increase in the cost of computations of secret shares and the increase in the amount of data transferred between the participating parties. In the context of my research, I Introduce A Systemic Comparison of Concurrent Multiparty Secret Sharing with SGD Regression and Classification in which I create and run experiments that compares different methods of MPC and different machine learning algorithms. The results of the experiments provide valuable insights into the practical viability of secure multiparty computation for machine learning applications. By comparing the performance of different secure computation methods, this research contributes to the understanding of the trade-offs between security and efficiency.