Secure Sharding Scheme of Blockchain Based on Reputation

In this paper, we focus on the constructions of adaptively secure identity-based encryption (IBE) from lattices and verifiable random function (VRF) with large input spaces. Existing constructions of these primitives suffer from low efficiency, whereas their counterparts with weaker guarantees (IBEs with selective security and VRFs with small input spaces) are reasonably efficient. We try to fill these gaps by developing new partitioning techniques that can be performed with compact parameters and proposing new schemes based on the idea.

Randomness is a critical issue in peer-to-peer networks because random numbers allow us to fairly select the candidates, resolve the lotteries, select block producers, etc. The difficulty is that each participant wants to verify that the random number was randomly generated, this led to the invention of verifiable random functions (VRF). The VRF is a pseudo-random function that provides a solution to blockchain-based random number generation. This paper focuses on the implementation of an elliptic curve-based VRF introduced by NIST called ECVRF-secp256r1 in Solidity. The algorithm verifies the randomness on-chain, tests the gas consumption at each stage, and compares it to the ECVRF-secp256k1.

As a new distributed computing paradigm, blockchain is rapidly evolving in areas such as digital finance and cryptocurrency. However, existing blockchain projects adopt different blockchain architectures and protocols, as a result it is difficult for different blockchain systems to flow value to each other or communicate information, and different blockchain systems become isolated islands. This has brought limitations to the usable range, function and scalability of blockchain technology. In order to solve this problem, and bulid trust between different blockchain networks, cross-chain technology has been proposed and received a lot of attention. In this paper we proposed a cross-chain mechanism based on verifiable random functions ( VRF ) and threshold cryptography, we name this scheme as Vger. In Vger, through VRF based cryptographic sortition to select management committee for escrow account and dynamic security setting mechanism, that optimize the security and efficiency about cross-chain transaction.

Proof of Work (PoW) is a fundamental and widely adopted consensus mechanism in blockchain systems that enables effective consensus in permissionless environments, solely through block propagation without additional message exchange between nodes. However, PoW can result in excessive energy consumption and network centralization. This paper proposes VRF-PoW, a novel consensus protocol that reduces energy consumption while preserving the decentralized and permissionless nature of PoW. The VRF-PoW method integrates Verifiable Random Functions (VRFs) into the PoW process, reducing network energy consumption. To prevent Sybil attacks, this work introduces a pre-PoW phase. Further, this work validates VRF-PoW via a theoretical analysis and numerical evaluation. The results demonstrate that VRF-PoW significantly reduces energy consumption compared to the traditional PoW, while maintaining decentralization, stable block generation, and network liveness.

An efficient Proof-of-Authority consensus scheme against cloning attacks

Blockchain technology, renowned for its decentralized, immutable, and transparent features, offers a reliable framework for trust in distributed systems. The growing popularity of consortium blockchains, which include public, private, hybrid, and consortium chains, stems from their balance of privacy and collaboration. A significant challenge in these systems is the scalability of consensus mechanisms, particularly when employing the Practical Byzantine Fault Tolerance (PBFT) algorithm. This review focuses on enhancing PBFT's scalability, a critical factor in the effectiveness of consortium chains. Innovations such as Boneh–Lynn–Shacham (BLS) signatures and Verifiable Random Functions (VRF) are highlighted for their ability to reduce algorithmic complexity and increase transaction throughput. The discussion extends to real-world applications, particularly in platforms like Hyperledger Fabric, showcasing the practical benefits of these advancements. This paper provides a concise overview of the latest methodologies that enhance the performance scalability of PBFT-based consortium chains, serving as a valuable resource for researchers and practitioners aiming to optimize these systems for high-performance demands.

Cohen, Goldwasser, and Vaikuntanathan (TCC’15) introduced the concept of aggregate pseudo-random functions (PRFs), which allow efficiently computing the aggregate of PRF values over exponential-sized sets. In this paper, we explore the aggregation augmentation on verifiable random function (VRFs), introduced by Micali, Rabin and Vadhan (FOCS’99), as well as its application to e-lottery schemes. We introduce the notion of static aggregate verifiable random functions (Agg-VRFs), which perform aggregation for VRFs in a static setting. Our contributions can be summarized as follows: (1) we define static aggregate VRFs, which allow the efficient aggregation of VRF values and the corresponding proofs over super-polynomially large sets; (2) we present a static Agg-VRF construction over bit-fixing sets with respect to product aggregation based on the q-decisional Diffie–Hellman exponent assumption; (3) we test the performance of our static Agg-VRFs instantiation in comparison to a standard (non-aggregate) VRF in terms of costing time for the aggregation and verification processes, which shows that Agg-VRFs lower considerably the timing of verification of big sets; and (4) by employing Agg-VRFs, we propose an improved e-lottery scheme based on the framework of Chow et al.’s VRF-based e-lottery proposal (ICCSA’05). We evaluate the performance of Chow et al.’s e-lottery scheme and our improved scheme, and the latter shows a significant improvement in the efficiency of generating the winning number and the player verification.

Proof of Stake (PoS) models are energy-efficient and require limited computational power. These features are critical in telemedicine environments, where resource-constrained devices must handle sensitive data securely. The growing need for auditable and privacy-preserving data storage in telemedicine underscores the importance of PoS models optimized for lightweight devices while complying with strict regulatory requirements, such as the Health Insurance Portability and Accountability Act (HIPAA).This study was guided by two research questions: (i) Which PoS models are lightweight and suitable for telemedicine? and (ii) What features make lightweight PoS models effective for privacy and efficiency in telemedicine? To address these questions, a systematic literature review (SLR) guided by the PICOC framework was conducted to investigate lightweight PoS models that can enhance privacy in telemedicine systems. Out of 2,394 papers studies screened, 55 were included in the analysis. The findings identified Algorand, Ouroboros Praos, Tendermint, Nxt, and Casper CBC as promising candidates. Key enabling features included lightweight voting mechanisms, such as Byzantine Agreement protocols and Verifiable Random Functions, as well as cryptographic techniques like symmetric encryption and multiparty computation. Performance metrics evaluated included latency, throughput, energy efficiency, and battery consumption, with Grey Relational Analysis ranking Algorand highest due to its low latency, high throughput, and minimal energy consumption.

Sensors in Body Area Network generate a huge amount of patient health data, but they have limited storage and computing capability. These data need to be transmitted to and aggregated by a gateway node which in turn can send the data to a cloud server. However, secure transmission, storage and access control of these data are of utmost importance to get useful insight from these data. Hence, there is a need for user authentication and key management scheme for the cloud assisted health-care services. Traditional password based or bio-metric based authentications mechanism suffer from security and privacy issues. State of the art multi-factor authentications schemes are the need of the hour. In this paper, a novel multi-factor authentication scheme has been designed using verifiable random functions. Thereafter, a key management scheme has been proposed. The performance analysis for the proposed scheme has been carried out and compared with related well known schemes.

Blind signatures allow a signer to issue signatures on messages chosen by the signature recipient. The main property is that the recipient’s message is hidden from the signer. There are many applications, including Chaum’s e-cash system and Privacy Pass, where no special distribution of the signed message is required, and the message can be random. Interestingly, existing notions do not consider this practical use case separately. In this paper, we show that constraining the recipient’s choice over the message distribution spawns a surprising new primitive that improves the well-established state-of-the-art. We formalize this concept by introducing the notion of non-interactive blind signatures ( $$\textsf{NIBS}$$ ). Informally, the signer can create a presignature with a specific recipient in mind, identifiable via a public key. The recipient can use her secret key to finalize it and receive a blind signature on a random message determined by the finalization process. The key idea is that online interaction between the signer and recipient is unnecessary. We show an efficient instantiation of $$\textsf{NIBS}$$ in the random oracle model from signatures on equivalence classes. The exciting part is that, in this case, for the recipient’s public key, we can use preexisting keys for Schnorr, ECDSA signatures, El-Gamal encryption scheme or even the Diffie-Hellman key exchange. Reusing preexisting public keys allows us to distribute anonymous tokens similarly to cryptocurrency airdropping. Additional contributions include tagged non-interactive blind signatures ( $$\textsf{TNIBS}$$ ) and their efficient instantiation. A generic construction in the random oracle or common reference string model based on verifiable random functions, standard signatures, and non-interactive proof systems.

The blockchain network architecture is a promising technology for constructing highly secure Internet of Things (IoT) networks. IoT networks typically consist of many sensors and actuators. Blockchain network technology can be applied to secure control robots in smart factories or for reliable drone delivery in smart cities. The distributed ledger and data block validation across blockchain networks guarantee the ultimate data security. However, the current blockchain technology is restricted in terms of its overall deployment across IoT networks. A general permissionless blockchain technology typically targets high-performance network nodes with sufficient computing power and memory space. A blockchain node with less computing power and memory, such as an IoT sensor or actuator, cannot employ blockchain technology as a fully functional node. A lightweight blockchain provides practical blockchain availability to IoT networks. We propose an operational advance to develop a lightweight blockchain for IoT networks. The opportunistic block validation optimizes the block validation process. It measures the network vulnerability based on the node reputation, block validation degree, number of fraudulent messages, and network stability. The reinforcement learning mechanism employed in the opportunistic block validation determines whether block validation is to be performed. Two separately developed reinforcement learning methods can increase the processing performance while maintain the transaction data integrity. In addition, the proposed blockchain technology is easily implementable because it adopts a Hyperledger development environment. Directly embedding the proposed blockchain middleware platform in small computing devices proves the practicability of the proposed block validation mechanism.

Telecom service providers are on a relentless task of development and upgradation of their networks to support increasing requirements of internet users. FTTX landline connections serves multiple users connected on a single line hence need more robust and high data rate capabilities. In FTTX network, link capacity allocation for a service node to aggregating node requires statistical study of usage patterns of customers of the service areas. Different service areas have different usage patterns hence their statistical distributions are different. In this article we have acquired a yearlong data of peak hour utilizations from customers of the largest FTTX service provider in Pakistan to develop a statistical model. We developed an empirical distribution of peak hours of the customers mapped on day clock. This distribution has been used in Monte Carlo computation to find maximum data rate requirements on aggregation nodes in comparison to subscribed data rates of all users on a service node. Further a forecasting model has been used to predict the growth in subscriber demands in different demographic segments for coming years. A combination of maximum possible data rate requirement at aggregation node and forecasted subscriber data rates led to develop a forecast of data rate requirement for next five years in different demographic segments.

A Mobile Ad hoc NETwork (MANET) is a collection of mobile nodes having no fixed topology and cooperating with each other. Due to these particularities, classical routing protocols cannot be used and some specific ones have been proposed. Because routing process is fundamental in a MANET deployment, it constitutes a privileged target of attackers. In this paper we propose a novel reputation-based clustering mechanism to locate malicious nodes and isolate them. In order to reduce network overhead and to handle network topology dynamicity, the proposed mechanism is based on a specific clustering environment. The clustering maintenance complexity is for its part reduced by the use of a reputation based delegation process allowing the cluster-head to delegate its privileges to a chosen cluster member in case of displacement or lack of energy. Moreover, node's reputation handling allows the detection and isolation of malicious nodes. Five modules constitute this mechanism: a monitoring module to detect malicious nodes, a reputation module to update reputation values, an isolation module to discard malicious nodes, an identity recognition module to assess alerts sources and a delegation module to allow clusterhead privileges delegation.

Objectives: Public key cryptography helps in achieving information security in wireless networks. Public key will be issued by CA (Certificate Authority). But having CA in wireless network makes it vulnerable to various attacks. Hence distributed cache was proposed where each node in network caches public key of some nodes existing in network in its cache and request to obtain keys not cached will be made via trusted nodes. Due to limited memory only few keys can be cached. There exists an optimal threshold value for caching keys so that equal number of both local nodes and remote nodes public keys can be cached. Methods: Random replacement policy was used for updating cache contents, where if a node’s local/ remote key cached ratio exceeds optimal ratio then a key will be randomly chosen from the cache and will be removed. Here we propose implementation of bloom filter in cache to cache public key of nodes existing in network. Findings: By using bloom filter of large size, more number of public keys can be cached thereby providing space and time advantage and reduces query cost with increased usage of memory. MAC (Media Access Control) id and PSN (Processor Serial Number) are used for uniquely identifying each node in network and are stored in encrypted format. When mismatch is found in encrypted MAC id and PSN, then that node is determined to be malicious node and its id will be notified to all nodes in network thus excluding the node from making any communication to other nodes in the network. Applications: Used for checking an element’s presence in the set thus reducing I/O lookup when large data set is used. Used as an alternative to cache as it uses limited memory more efficiently than cache does.

In this paper, we address the issue of the presence of malicious and selfish nodes in Vehicular Ad Hoc Networks (VANETs). Malicious nodes spread false and forged messages, while selfish nodes only cooperate for their own interest. To deal with this, we propose DTM <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> , a Distributed Trust Model inspired by Spence's Job Market model from Economics. In our model, a sender node transmits a signal with its message. This signal represents a guarantee of the truthfulness of the message for the potential receivers. In order to use the signal, the sender node has to pay a cost, which depends on the value of the signal and its own behavior. Therefore, the worse the behavior of the sender node, the more expensive the signal cost. This model deters the sender nodes from acting as malicious nodes. Similarly, cooperation of the sender nodes is rewarded proportionally to the signal's value. We validated DTM <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> via extensive simulation in an urban scenario. We show that our approach is able to detect and evict gradually all malicious nodes in a network composed of 25%, and 50% of them. Moreover, our solution greatly decreases the ratio of corrupted and false data sent through a network to levels as low as 0%, and it increases the participation ratio of selfish nodes by 20%.