A Layered Architecture and Taxonomy for Blockchain-empowered Reputation-based Reward Systems

Blockchain based rating and review systems have changed the operational structure of the traditional market by introducing characteristics like immutability, security, anonymity etc. to liberate users from potential malicious acts of sellers such as altering and hiding ratings or reviews, collusion with users or service providers. The lack of standardization for developing decentralized applications does not depict flow of information and cataloguing of specific functions and roles for a particular set of tasks. The development of decentralized applications for e-commerce systems is in its immature age of progress and has lack of interoperable sharing of data and workflows for new innate systems. Thus, it is significant to catalogue blockchain-based rating and review systems by identifying key parameters to generate a taxonomy and develop a conceptual layered framework for identifying core components and their interaction. This manuscript presents a substantial analysis of existing blockchain-empowered reputation-based reward systems. It uses an iterative approach following observed to rational and rational to observed for taxonomy development. The analysis results identify 11 key parameters for categorizing systems and propose a 4 layered architecture to signify IPFS, P2P network, Blockchain and DApps. The proposed model identifies underlying subsystems, their services, and their interaction. The new taxonomy identifies natural roadmaps in system development process. This study is key because it allows developers to design new reputation-based reward framework in different dimensions by following an open workflow with a common understanding of underlying core entities

Majority is Not Required: A Rational Analysis of the Private Double-Spend Attack from a Sub-Majority Adversary

We study the incentives behind double-spend attacks on Nakamoto-style Proof-of-Work cryptocurrencies. In these systems, miners are allowed to choose which transactions to reference with their block, and a common strategy for selecting transactions is to simply choose those with the highest fees. This can be problematic if these transactions originate from an adversary with substantial (but less than 50\%) computational power, as high-value transactions can present an incentive for a rational adversary to attempt a double-spend attack if they expect to profit. The most common mechanism for deterring double-spend attacks is for the recipients of large transactions to wait for additional block confirmations (i.e., to increase the attack cost). We argue that this defense mechanism is not satisfactory, as the security of the system is contingent on the actions of its users. Instead, we propose that defending against double-spend attacks should be the responsibility of the miners; specifically, miners should limit the amount of transaction value they include in a block (i.e., reduce the attack reward). To this end, we model cryptocurrency mining as a mean-field game in which we augment the standard mining reward function to simulate the presence of a rational, double-spending adversary. We design and implement an algorithm which characterizes the behavior of miners at equilibrium, and we show that miners who use the adversary-aware reward function accumulate more wealth than those who do not. We show that the optimal strategy for honest miners is to limit the amount of value transferred by each block such that the adversary's expected profit is 0. Additionally, we examine Bitcoin's resilience to double-spend attacks. Assuming a 6 block confirmation time, we find that an attacker with at least 25% of the network mining power can expect to profit from a double-spend attack

Study on quantitative design for dynamic blockchain-based computing

This research proposes novel embedded Markovian queueing model-based quantitative models in order to establish a theoretical foundation to design a dynamic blockchain-based computing system with a specific interest in Ethereum. The proposed models commonly assume variable bulk arrivals of transactions in Poisson distribution, i.e., M^(1,n), where n the number of slots across all the mined transactions to be posted in a block or the current block. Firstly, a baseline model is proposed to have a static bulk service of transactions in exponential time, i.e., M^n, for posting the transactions in the current block, referred to as Variable Bulk Arrival and Static Bulk Service (VBASBS) queueing model of the M^(1,n)/M^n/1 type, in which note that n is fixed in order to demonstrate a static chain in terms of the size of the block. Secondly, an adaptive chain model, as a solution of dynamic blockchain in a reactive manner, is proposed based on a Variable Bulk Arrival and Variable Bulk Service (VBAVBS) queueing model of the M^(1,n)/M^(1,i,n)/1 type to provide a quantitative approach to design an adaptive chain that dynamically adapts the size of the block to varying performance trends, in which a state transitions from i back to 0, where 0<i</=n, are tracked in order to demonstrate the dynamically adaptive size of the block. Lastly, an asynchronous chain model, as a solution of dynamic blockchain in a proactive manner, is proposed based on a Variable Bulk Arrival and Asynchronous Bulk Service (VBAABS) queueing model is developed and presented to study and demonstrate the fully asynchronous and staged asynchronous chains. The analytical models are simulated extensively to compare the basic performances of the proposed models such as the average transaction waiting time, the average number of slots per block, and throughput. Further, extensive experiments are conducted in order to validate the analytical results by redesigning the source code of Ethereum to implement and demonstrate each of the proposed chains such as the baseline, the adaptive, the fully-asynchronous and the staged-asynchronous chains. The analytical results and the experimental results will be compared and discussed extensively

Blockchain Software Verification and Optimization

In the last decade, blockchain technology has undergone a strong evolution. The maturity reached and the consolidation obtained have aroused the interest of companies and businesses, transforming it into a possible response to various industrial needs. However, the lack of standards and tools for the development and maintenance of blockchain software leaves open challenges and various possibilities for improvements. The goal of this thesis is to tackle some of the challenges proposed by blockchain technology, to design and implement analysis, processes, and architectures that may be applied in the real world. In particular, two topics are addressed: the verification of the blockchain software and the code optimization of smart contracts. As regards the verification, the thesis focuses on the original developments of tools and analyses able to detect statically, i.e. without code execution, issues related to non-determinism, untrusted cross-contracts invocation, and numerical overflow/underflow. Moreover, an approach based on on-chain verification is investigated, to proactively involve the blockchain in verifying the code before and after its deployment. For the optimization side, the thesis describes an optimization process for the code translation from Solidity language to Takamaka, also proposing an efficient algorithm to compute snapshots for fungible and non-fungible tokens. The results of this thesis are an important first step towards improving blockchain software development, empirically demonstrating the applicability of the proposed approaches and their involvement also in the industrial field