Since the introduction of Bitcoin, numerous studies on Bitcoin mining attacks have been conducted, and as a result, many countermeasures to these attacks have been proposed. The reputation-based mining paradigm is a comprehensive countermeasure solution to this problem with the goal of regulating the mining process and preventing mining attacks. This is accomplished by incentivizing miners to avoid dishonest mining strategies using reward and punishment mechanisms. This model was validated solely based on game theoretical analyses, and the real-world implications of this model are not known due to the lack of empirical data. To shed light on this issue, we designed a simulated mining platform to examine the effectiveness of the reputation-based mining paradigm through data analysis. We implemented block withholding attacks in our simulation and ran the following three scenarios: Reputation mode, non-reputation mode, and no attack mode. By comparing the results from these three scenarios, interestingly, we found that the reputation-based mining paradigm decreases the number of block withholding attacks, and as a result, the actual revenue of individual miners becomes closer to their theoretical expected revenue. In addition, we observed that the confidence interval test can effectively detect block withholding attacks; however, the test also results in a small number of false positive cases. Since the effectiveness of the reputation-based model relies on attack detection, further research is needed to investigate the effect of this model on other dishonest mining strategies.

Consensus protocols are a key feature in decentral-ized systems where multiple unreliable nodes operate, e.g., in Blockchain technologies with many worldwide applications such as supply chain management, cryptocurrencies and information sharing. ISRaft is a consensus protocol built upon Raft, a previously developed protocol that is used for replicated state machines when a group of nodes is required to achieve a consensus related to the state of the machine. This paper therefore proposes an alternative version of the ISRaft consensus protocol to allow communication among nodes in a secured fashion while maintaining the security features of the original ISRaft algorithm even in the presence of adversarial nodes. The proposed model utilizes a trust parameter to enforce cooperation, i.e., a trust value is assigned to each node to prevent malicious activities over time. This is a practical solution for autonomous units with resource-constrained devices where a regular encrypted communication method can negatively affect the system performance.

Recent studies indicate that people are negatively predisposed toward utilizing autonomous systems. These findings highlight the necessity of conducting research to better understand the evolution of trust between humans and growing autonomous technologies such as self-driving cars (SDC). This research presents a new approach for real-time trust measurement between passengers and SDCs. We utilized a new structured data collection approach along with a virtual reality SDC simulator to understand how various autonomous driving scenarios can increase or decrease human trust and how trust can be re-built in the case of incidental failures. To verify our methodology, we designed and conducted an empirical experiment on 50 human subjects. The results of this experiment indicated that most subjects could rebuild trust during a reasonable time frame after the system demonstrated faulty behavior. Our analysis showed that this approach is highly effective for collecting real-time data from human subjects and lays the foundation for more-involved future research in the domain of human trust and autonomous driving.

In distributed storage systems, documents are shared among multiple Cloud providers and stored within their respective storage servers. In social secret sharing-based distributed storage systems, shares of the documents are allocated according to the trustworthiness of the storage servers. This paper proposes a trust mechanism using machine learning techniques to compute evidence-based trust values. Our mechanism mitigates the effect of colluding storage servers. More precisely, it becomes possible to detect unreliable evidence and establish countermeasures in order to discourage the collusion of storage servers. Furthermore, this trust mechanism is applied to the social secret sharing protocol AS^3, showing that this new evidence-based trust mechanism enhances the protection of the stored documents.

Cryptographic architectures provide different security properties to sensitive usage models. However, unless reliability of architectures is guaranteed, such security properties can be undermined through natural or malicious faults. In this paper, two underlying block ciphers which can be used in authenticated encryption algorithms are considered, i.e., light encryption device and high security and lightweight block ciphers. The former is of the Advanced Encryption Standard type and has been considered area-efficient, while the latter constitutes a Feistel network structure and is suitable for low-complexity and low-power embedded security applications. In this paper, we propose efficient error detection architectures including variants of recomputing with encoded operands and signature-based schemes to detect both transient and permanent faults. Authenticated encryption is applied in cryptography to provide confidentiality, integrity, and authenticity simultaneously to the message sent in a communication channel. In this paper, we show that the proposed schemes are applicable to the case study of simple lightweight CFB for providing authenticated encryption with associated data. The error simulations are performed using Xilinx Integrated Synthesis Environment tool and the results are benchmarked for the Xilinx FPGA family Virtex-7 to assess the reliability capability and efficiency of the proposed architectures.

In this paper, a new proactive verifiable secret sharing (PVSS) scheme is proposed in an active mobile adversary setting. To the best of our knowledge, the only unconditionally secure PVSS is proposed in ASIACRYPT'02 by D'Arco and Stinson. In this protocol, the authors assume the existence of private channels as well as an authenticated broadcast channel. This scheme uses symmetric bivariate polynomials for secret sharing, i.e., each share is a polynomial over a finite field rather than a single field element, under the assumption that t <; n/4 where t is the threshold. We propose a new PVSS scheme with prominent properties using a simple detection method along with a novel recovery-and-renewal technique. First of all, we only consider private channels in our setting. In addition, our scheme can tolerate t <; n/3 corrupted players. Although we utilize a VSS scheme as a subprotocol in our scheme, shares of the secret are single field elements after the initialization by the dealer.

Abstract The concept of social secret sharing was introduced in 2010 by Nojoumian et al. In the scheme of Nojoumian et al. (called SSS), the number of shares allocated to each party depends on the player's reputation and the way he interacts with other parties. In other words, weights of the players are periodically adjusted such that cooperative participants receive more shares compared with non‐cooperative parties. As our contribution, we propose an ideal social secret sharing in which the size of each player's share is equal to the size of the secret. This property will be achieved using hierarchical threshold secret sharing. We show that the proposed scheme is secure in a passive adversary model. Compared with SSS, our proposed scheme is more efficient in terms of the share size, communication complexity, and computational complexity of the “sharing” protocol. However, the “social tuning” and “reconstruction” protocols of SSS are computationally more efficient than those of the proposed scheme. Depending on the number of execution of social tuning protocol, this might be a reasonable compromise because the reconstruction protocol is executed only once throughout the secret's lifetime. Copyright © 2016 John Wiley & Sons, Ltd.

Designing a sealed-bid auction protocol is a challenging problem in the field of applied cryptography. In the last couple of decades, numerous protocols have been proposed in the literature where each one has its own property in terms of the security model, communication and computation complexities. To the best of our knowledge, there has been no study to implement and compare a similar class of sealed-bid auction protocols. This paper therefore implements and evaluates five different Dutch-style sealed-bid auction protocols, of which three protocols are computationally secure and two protocols are unconditionally secure. It mainly focuses on the computational cost of the initialization and verification phases of these privacy-preserving protocols.

Rational secret sharing shows that, in a setting with rational players, secret sharing and multiparty computation are only possible if the actual secret reconstruction round remains unknown to the parties. However, in socio-rational secret sharing, players not only are rational but also are foresighted. In other words, the secret sharing game is repeatedly played and players are only invited to each game based on their reputation. This social reinforcement stimulates the players to be cooperative. As our contribution, we revisit socio-rational secret sharing and generalize it from the utility computation aspect. We show that, in (2; 2) and (t; n) socio-rational secret sharing, it is always in players' best interest to cooperate using our new utility function.

We first review the notion of social secret sharing and its trust function. We then illustrate how this construction can be used in cloud computing to create a self-organizing environment. In fact, we show distributed secure systems using threshold secret sharing can be adjusted automatically based on the resource availability of the cloud providers. Accordingly, we propose a new trust function with social characteristics in order to improve the existing social secret sharing scheme.

We introduce the notion of a social secret sharing scheme, in which shares are allocated based on a player's reputation and the way he interacts with other participants. During the social tuning phase, weights of players are adjusted such that participants who cooperate will end up with more shares than those who defect.

The authors introduce the notion of a ‘social secret sharing scheme’, in which shares are allocated based on a player's reputation and the way he/she interacts with other participants. During the social tuning phase, weights of players are adjusted such that participants who cooperate will end up with more shares than those who defect. Alternatively, newcomers are able to be enrolled in the scheme while corrupted players are disenrolled immediately. In other words, this scheme proactively renews shares at each cycle without changing the secret, and allows trusted participants to gain more authority. The motivation is that, in real-world applications, components of a secure scheme may have different levels of importance (i.e. the number of shares a player has) as well as reputation (i.e. cooperation with other players for the share renewal or secret recovery). Therefore a good construction should balance these two factors, respectively. In the proposed schemes, both the passive and active mobile adversaries are considered in an unconditionally secure setting.

This paper aims at comparing Genetic Algorithm (GA) and Guided Local Search (GLS) methods so as to scrutinize their behaviors. Authors apply the GLS program with the Fast Local Search (FLS), developed at University of Essex, and implement a genetic algorithm with partially-mapped and order crossovers, reciprocal and inversion mutations, and rank and tournament selections in order to experiment with various Travelling Salesman Problems. The paper then ends up with two prominent conclusions regarding the performance of these meta-heuristic techniques over wide range of symmetric-TSP instances. First, the GLS-FLS strategy on the s-TSP instances yields the most promising performance in terms of the near-optimality and the mean CPU time. Second, the GA results are comparable to GLS-FLS outcomes on the same s-TSP instances. In the other word, the GA is able to generate near optimal solutions with some compromise in the CPU time.