TrustChain is capable of creating trusted transactions among strangers without central control. This enables new areas of blockchain use with a focus on building trust between individuals. Our innovative approach offers scalability, openness and Sybil-resistance while replacing proof-of-work with a mechanism to establish the validity and integrity of transactions. TrustChain is a permission-less tamper-proof data structure for storing transaction records of agents. We create an immutable chain of temporally ordered interactions for each agent. It is inherently parallel and every agent creates his own genesis block. TrustChain includes a novel Sybil-resistant algorithm named NetFlow to determine trustworthiness of agents in an online community. NetFlow ensures that agents who take resources from the community also contribute back. We demonstrate that irrefutable historical transaction records offer security and seamless scalability, without requiring global consensus. Experimentation shows that the transaction throughput of TrustChain surpasses that of traditional blockchain architectures like Bitcoin. We show by using extracted data from a live network that TrustChain has sufficient informativeness to identify freeriders, leading to refusal of service.

Matchmaking is a core enabling element in peer-to-peer markets. To date, matchmaking is predominantly performed by proprietary algorithms, fully controlled by market operators. This raises fairness concerns as market operators effectively can hide, prioritize, or delay the orders of specific users. Blockchain technology has been proposed as an alternative for fair matchmaking without a trusted operator but is still vulnerable to specific fairness attacks. We present MATCH, a decentralized middleware for fair matchmaking in peer-to-peer markets. By decoupling the dissemination of potential matches from the negotiation of trade agreements, MATCH empowers end-users to make their own educated decisions and to engage in direct negotiations with trade partners. This approach makes MATCH highly resilient against malicious matchmakers that deviate from a specific matching policy We implement MATCH and evaluate our middleware using real-world ride-hailing and asset trading workloads. It is demonstrated that MATCH maintains high matching quality, even when 75% of all matchmakers is malicious. We also show that the bandwidth usage and order fulfil latency of MATCH is orders of magnitude lower compared to matchmaking on an Ethereum blockchain.

The landscape of electronic marketplaces has been monopolized by a handful of market operators that have accumulated tremendous power during the last decades. This trend raises concerns about fairness and market manipulation by these operators acting as gatekeepers. These concerns have recently been outlined in the EU Digital Markets Act (DMA). In this work, we highlight how technological logic of separation understood in the framework of decentralization can address manipulation concerns. As a first step, we devise a reference model of electronic marketplaces, containing six functional components, and outline how control over these components enables different manipulative practices by gatekeepers. We identify two dimensions of decentralization that can counterbalance monopolistic abuse of marketplace components. We then present a software implementation of our reference model and demonstrate how decentralization and unbundling of market components can alleviate manipulation and fairness concerns. We end our work with a review of related approaches and conclude that modular and interoperable marketplaces can enable an open ecosystem of fair electronic markets envisioned by the DMA.

We present Epidemic Learning (EL), a simple yet powerful decentralized learning (DL) algorithm that leverages changing communication topologies to achieve faster model convergence compared to conventional DL approaches. At each round of EL, each node sends its model updates to a random sample of s other nodes (in a system of n nodes). We provide an extensive theoretical analysis of EL, demonstrating that its changing topology culminates in superior convergence properties compared to the state-of-the-art (static and dynamic) topologies. Considering smooth non-convex loss functions, the number of transient iterations for EL, i.e., the rounds required to achieve asymptotic linear speedup, is in O(n3/s2) which outperforms the best-known bound O(n3) by a factor of s2, indicating the benefit of randomized communication for DL. We empirically evaluate EL in a 96-node network and compare its performance with state-of-the-art DL approaches. Our results illustrate that EL converges up to 1.7x quicker than baseline DL algorithms and attains 2.2% higher accuracy for the same communication volume.