Webs represents a n ambitious attempt to democratize access to financial instruments and reimagine economic coordination. At its core, Web3 envisions a public infrastructure where users actively contribute to and share control of the systems they depend upon. Yet the gap between this vision and today's implementations reveals deep architectural flaws.

This thesis revisits Web3's foundations from first principles. Our investigation reveals that persistent vulnerabilities like transaction manipulation, Sybil attacks, and selfish behavior stem from misguided assumptions about participant behavior and misplaced focus on consensus alone.

Trustworthy Web3 foundations must account for the fact that protocols can be forked, validators run different software versions, and participants operate under diverse incentives. These realities demand accountability and incentive alignment mechanisms beyond traditional security models.

Manipulation of user transactions by miners in permissionless blockchain systems is a growing concern. This problem is a pervasive and systemic issue that incurs high costs for users of decentralised applications and is known as Miner Extractable Value (MEV). Furthermore, transaction manipulations create other issues such as congestion, higher fees, and system instability. Detecting transaction manipulations is difficult, even though it is known that they originate from the pre-consensus phase of transaction selection for building blocks, at the base layer of blockchain protocols. In this paper, we summarize known transaction manipulation attacks. We present LO, an accountable base layer protocol designed to detect and mitigate transaction manipulations. LO is built around the accurate detection of transaction manipulations and assignment of blame at the granularity of a single mining node. LO forces miners to log all the transactions they receive into a secure mempool data structure and to process them in a verifiable manner. Overall, LO quickly and efficiently detects censorship, injection or re-ordering attempts. Our performance evaluation shows that LO is also practical and only introduces a marginal performance overhead.

Decentralized reputation schemes present a promising area of experimentation in blockchain applications. These solutions aim to overcome the shortcomings of simple monetary incentive mechanisms of naive tokenomics. However, there is a significant research gap regarding the limitations and benefits of such solutions. We formulate these trade-offs as a conjecture on the irreconcilability of three desirable properties of the reputation system in this context. Such a system can not be simultaneously generalizable, trustless, and Sybil resistant. To handle the limitations of this trilemma, we propose MeritRank: Sybil tolerant feedback aggregation mechanism for reputation. Instead of preventing Sybil attacks, our approach successfully bounds the benefits of these attacks. Using a dataset of participants’ interactions in MakerDAO, we run experiments to demonstrate Sybil tolerance of MeritRank. Decay parameters of reputation in MeritRank: transitivity decay and connectivity decay, allow for a fine-tuning of desirable levels of reputation utility and Sybil tolerance in different use contexts.

The growing number of implementations of blockchain systems stands in stark contrast with still limited research on a systematic comparison of performance characteristics of these solutions. Such research is crucial for evaluating fundamental trade-offs introduced by novel consensus protocols and their implementations. These performance limitations are commonly analyzed with ad-hoc benchmarking frameworks focused on the consensus algorithm of blockchain systems. However, comparative evaluations of design choices require macro-benchmarks for uniform and comprehensive performance evaluations of blockchains at the system level rather than performance metrics of isolated components. To address this research gap, we implement Gromit, a generic framework for analyzing blockchain systems. Gromit treats each system under test as a transaction fabric where clients issue transactions to validators. We use Gromit to conduct the largest blockchain study to date, involving seven representative systems with varying consensus models. We determine the peak performance of these systems with a synthetic workload in terms of transaction throughput and scalability and show that transaction throughput does not scale with the number of validators. We explore how robust the subjected systems are against network delays and reveal that the performance of permissoned blockchain is highly sensitive to network conditions.

Decentralized systems offer alternatives to Big Tech. However, maintaining availability and correctness despite faults and manipulations in decentralized settings is challenging. In this paper, we introduce a collaborative model that is capable of exposing all observable lying, all cheating, and all faults, while only requiring merely unreliable message exchange. Our model is based on conflicting operations on arbitrary data, set reconciliation, and conflict resolution strategies to deal with branches. It is sufficiently general to support applications like Wikipedia, Github, and Datahub in a non-profit, collaborative, and decentralized form. Our protocol guarantees strong convergence despite any Byzantine nodes. We exhibit four conflict resolution strategies that cover the spectrum of possible use cases. A remarkable property of our model is that two honest nodes are guaranteed to converge despite an arbitrary-large number of faulty nodes.