Levels of Decentralization and Trust in Cryptocurrencies: Consensus, Governance and Applications

Since the apparition of Bitcoin, decentralization has become an ideal praised almost religiously. Indeed, removing the need for a central authority prevents many forms of abuse that could be performed by a trusted third party, especially when there are no transparency and accountability mechanisms in place. Decentralization is however a very subtle concept that has limits. In this thesis, we look at the decentralization of blockchains at three different levels. First we look at the consensus protocol, which is the heart of any decentralized system. The Nakamoto protocol, used by Bitcoin, has been shown to induce centralization through the shift to mining pools. Additionally, it is heavily criticized for the enormous amount of energy it requires. We propose a protocol, Fantômette, that incorporates incentives at its core and that consumes much less energy than Bitcoin and other proof-of-work based cryptocurrencies. If the consensus protocol makes it possible to decentralize the enforcement of rules in a cryptocurrency, there is still the question of who decides on the rules. Indeed, if a central authority is able to determine what those rules are then the fact that they are enforced in a decentralized way does not make it a decentralized system. We study the governance structure of Bitcoin and Ethereum by making measurements of their GitHub repositories and providing quantitative ways to compare their level of centralization by using appropriate metrics based on centrality measures. Finally, many applications are now built on top of blockchains. These can also induce or straightforwardly lead to centralization, for example by requiring that users register their identities to comply with regulations. We show how identities can be registered on blockchains in a decentralized and privacy-preserving way

SoK: Diving into DAG-based Blockchain Systems

Blockchain plays an important role in cryptocurrency markets and technology services. However, limitations on high latency and low scalability retard their adoptions and applications in classic designs. Reconstructed blockchain systems have been proposed to avoid the consumption of competitive transactions caused by linear sequenced blocks. These systems, instead, structure transactions/blocks in the form of Directed Acyclic Graph (DAG) and consequently re-build upper layer components including consensus, incentives, \textit{etc.} The promise of DAG-based blockchain systems is to enable fast confirmation (complete transactions within million seconds) and high scalability (attach transactions in parallel) without significantly compromising security. However, this field still lacks systematic work that summarises the DAG technique. To bridge the gap, this Systematization of Knowledge (SoK) provides a comprehensive analysis of DAG-based blockchain systems. Through deconstructing open-sourced systems and reviewing academic researches, we conclude the main components and featured properties of systems, and provide the approach to establish a DAG. With this in hand, we analyze the security and performance of several leading systems, followed by discussions and comparisons with concurrent (scaling blockchain) techniques. We further identify open challenges to highlight the potentiality of DAG-based solutions and indicate their promising directions for future research.Comment: Full versio

Blockchain Nash Dynamics and the Pursuit of Compliance

We study "Nash dynamics" in the context of adversarial deviations in blockchain protocols. We introduce a formal model, within which one can assess whether the Nash dynamics can lead utility-maximizing participants to defect from the "honest" protocol operation, towards variations that exhibit one or more undesirable infractions that affect protocol security, like abstaining from participation and producing conflicting protocol histories. Blockchain protocols that lead to no such infraction states are deemed compliant. Armed with this model, we evaluate the compliance of various Proof-of-Work (PoW) and Proof-of-Stake (PoS) protocol families, under different utility functions and reward schemes, leading to the following results: i) PoW and PoS protocols exhibit different compliance behavior, depending on the lossiness of the network; ii) PoS ledgers can be compliant w.r.t. one realistic infraction (producing conflicting messages) but non-compliant (hence non-equilibria) w.r.t. others (abstaining or an attack we call selfish signing); iii) considering externalities, like exchange rate fluctuations, we quantify the benefit of economic penalties in the context of PoS protocols as mitigation for particular infractions that affect protocol security

A Survey on Consensus Mechanisms and Mining Strategy Management in Blockchain Networks

© 2013 IEEE. The past decade has witnessed the rapid evolution in blockchain technologies, which has attracted tremendous interests from both the research communities and industries. The blockchain network was originated from the Internet financial sector as a decentralized, immutable ledger system for transactional data ordering. Nowadays, it is envisioned as a powerful backbone/framework for decentralized data processing and data-driven self-organization in flat, open-access networks. In particular, the plausible characteristics of decentralization, immutability, and self-organization are primarily owing to the unique decentralized consensus mechanisms introduced by blockchain networks. This survey is motivated by the lack of a comprehensive literature review on the development of decentralized consensus mechanisms in blockchain networks. In this paper, we provide a systematic vision of the organization of blockchain networks. By emphasizing the unique characteristics of decentralized consensus in blockchain networks, our in-depth review of the state-of-the-art consensus protocols is focused on both the perspective of distributed consensus system design and the perspective of incentive mechanism design. From a game-theoretic point of view, we also provide a thorough review of the strategy adopted for self-organization by the individual nodes in the blockchain backbone networks. Consequently, we provide a comprehensive survey of the emerging applications of blockchain networks in a broad area of telecommunication. We highlight our special interest in how the consensus mechanisms impact these applications. Finally, we discuss several open issues in the protocol design for blockchain consensus and the related potential research directions

KRNC: New Foundations for Permissionless Byzantine Consensus and Global Monetary Stability

This paper applies biomimetic engineering to the problem of permissionless Byzantine consensus and achieves results that surpass the prior state of the art by four orders of magnitude. It introduces a biologically inspired asymmetric Sybil-resistance mechanism, Proof-of-Balance, which can replace symmetric Proof-of-Work and Proof-of-Stake weighting schemes. The biomimetic mechanism is incorporated into a permissionless blockchain protocol, Key Retroactivity Network Consensus ("KRNC"), which delivers ~40,000 times the security and speed of today's decentralized ledgers. KRNC allows the fiat money that the public already owns to be upgraded with cryptographic inflation protection, eliminating the problems inherent in bootstrapping new currencies like Bitcoin and Ethereum. The paper includes two independently significant contributions to the literature. First, it replaces the non-structural axioms invoked in prior work with a new formal method for reasoning about trust, liveness, and safety from first principles. Second, it demonstrates how two previously overlooked exploits, book-prize attacks and pseudo-transfer attacks, collectively undermine the security guarantees of all prior permissionless ledgers.Comment: 104 page

Predictive Modeling for Fair and Efficient Transaction Inclusion in Proof-of-Work Blockchain Systems

This dissertation investigates the strategic integration of Proof-of-Work(PoW)-based blockchains and ML models to improve transaction inclusion, and consequently molding transaction fees, for clients using cryptocurrencies such as Bitcoin. The research begins with an in-depth exploration of the Bitcoin fee market, focusing on the interdependence between users and miners, and the emergence of a fee market in PoW-based blockchains. Our observations are used to formalize a transaction inclusion pattern. To support our research, we developed the Blockchain Analytics System (BAS) to acquire, store, and pre-process a local dataset of the Bitcoin blockchain. BAS employs various methods for data acquisition, including web scraping, web browser APIs, and direct access to the blockchain using Bitcoin Core software. We utilize time-series data analysis as a tool for predicting future trends, and transactions are sampled on a monthly basis with a fixed interval, incorporating a notion of relative time represented by block-creation epochs. We create a comprehensive model for transaction inclusion in a PoW-based blockchain system, with a focus on factors of revenue and fairness. Revenue serves as an incentive for miners to participate in the network and validate transactions, while fairness ensures equal opportunity for all users to have their transactions included upon paying an adequate fee value. The ML architecture used for prediction consists of three critical stages: the ingestion engine, the pre-processing stage, and the ML model. The ingestion engine processes and transforms raw data obtained from the blockchain, while the pre-processing phase transforms the data further into a suitable form for analysis, including feature extraction and additional data processing to generate a complete dataset. Our ML model showcases its effectiveness in predicting transaction inclusion, with an accuracy of more than 90%. Such a model enables users to save at least 10% on transaction fees while maintaining a likelihood of inclusion above 80%. Furthermore, adopting such model based on fairness and revenue, demonstrates that miners' average loss is never higher than 1.3%. Our research proves the efficacy of a formal transaction inclusion model and ML prototype in predicting transaction inclusion. The insights gained from our study shed light on the underlying mechanisms governing miners' decisions, improving the overall user experience, and enhancing the trust and reliability of cryptocurrencies. Consequently, this enables Bitcoin users to better select suitable fees and predict transaction inclusion with notable precision, contributing to the continued growth and adoption of cryptocurrencies

Decentralized Polling with Respectable Participants

International audienceWe consider the polling problem in a social network: participants express support for a given option and expect an outcome reflecting the opinion of the majority. Individuals in a social network care about their reputation: they do not want their vote to be disclosed or any potential misbehavior to be publicly exposed. We exploit this social aspect of users to model dishonest behavior, and show that a simple secret sharing scheme, combined with lightweight verification procedures, enables private and accurate polling without requiring any central authority or cryptography. We present DPol, a simple and scalable distributed polling protocol in which misbehaving nodes are exposed with positive probability and in which the probability of honest participants having their privacy violated is traded off with the impact of dishonest participants on the accuracy of the polling result. The trade-off is captured by a generic parameter of the protocol, an integer k called the privacy parameter. In a system of N nodes with B dishonest participants, the probability of disclosing a participant's vote is bounded by (B/N)^{k+1}, whereas the impact on the score of each polling option is at most (3k+2) B with high probability when dishonest users are a minority (i.e., B < N/2), assuming nodes are uniformly spread across groups used by the system. When dishonest users are few (i.e., B < sqrt{N}), the impact bound holds deterministically and our protocol is asymptotically accurate: there is negligible difference between the true result score of the poll and the outcome of our protocol. To demonstrate the practicality of DPol, we report on its deployment on 400 PlanetLab nodes. The relative error of the polling result is less than 10% when faced with the message loss, crashes and delays inherent in PlanetLab. Our experiments show that the impact on the score of each polling option by dishonest nodes is (2k+1) B on average, consistently lower that the theoretical bound of (3k+2) B