Quantum entanglement enables important computing applications such as quantum key distribution. Based on quantum entanglement, quantum networks are built to provide long-distance secret sharing between two remote communication parties. Establishing a multi-hop quantum entanglement exhibits a high failure rate, and existing quantum networks rely on trusted repeater nodes to transmit quantum bits. However, when the scale of a quantum network increases, it requires end-to-end multi-hop quantum entanglements in order to deliver secret bits without letting the repeaters know the secret bits. This work focuses on the entanglement routing problem, whose objective is to build long-distance entanglements via untrusted repeaters for concurrent source-destination pairs through multiple hops. Different from existing work that analyzes the traditional routing techniques on special network topologies, we present a comprehensive entanglement routing model that reflects the differences between quantum networks and classical networks as well as a new entanglement routing algorithm that utilizes the unique properties of quantum networks. Evaluation results show that the proposed algorithm Q-CAST increases the number of successful long-distance entanglements by a big margin compared to other methods. The model and simulator developed by this work may encourage more network researchers to study the entanglement routing problem.

Payment channel networks (PCNs) have been designed and utilized to address the scalability challenge and throughput limitation of blockchains. It provides a high-throughput solution for blockchain-based payment systems. However, such “layer-2” blockchain solutions have their own problems: payment channels require a separate deposit for each channel of two users. Thus it significantly locks funds from users into particular channels without the flexibility of moving these funds across channels. In this paper, we proposed Aggregated Payment Channel Network (APCN), in which flexible funds are used as a per-user basis instead of a per-channel basis. To prevent users from misbehaving such as double-spending, APCN includes mechanisms that make use of hardware trusted execution environments (TEEs) to control funds, balances, and payments. The distributed routing protocol in APCN also addresses the congestion problem to further improve resource utilization. Our prototype implementation and simulation results show that APCN achieves significant improvements on transaction success ratio with low routing latency, compared to even the most advanced PCN routing.

The widely deployed IoT devices in various applications, such as smart homes and smart factories, pose new privacy concerns. IoT devices typically capture users’ activities or collect information from their surroundings and then send the information to remote cloud servers, exposing private information to passive adversaries by looking at the packet headers. Thus, in an enhanced IoT security protocol, protecting privacy also requires hiding packet headers and other traffic metadata. This work presents the LOIS framework, a packet-level packet header protector based on efficient one-time keystreams. LOIS allows IoT devices to efficiently hide the IP and port information in packet headers while allowing the cloud to recover the original packet headers. Besides, LOIS can easily integrate with existing IoT traffic padding algorithms to hide traffic patterns. We implement LOIS on commodity servers running in a public cloud. Our experimental results show that LOIS only introduces moderate overhead. For example, results show that LOIS only incurs about 250–365 ns end-to-end latency on average for the upload traffic, which is 80%–90% less than that of IPsec.

Distributed Hash Tables (DHTs) are pivotal in numerous high-impact key-value applications built on distributed networked systems, offering a decentralized architecture that avoids single points of failure and improves data availability. Despite their widespread utility, DHTs face substantial challenges in handling range queries, which are crucial for applications such as LLM serving, distributed storage, databases, content delivery networks, and blockchains. To address this limitation, we present LEAD, a novel system incorporating learned models within DHT structures to significantly optimize range query performance. LEAD utilizes recursive machine learning models as the Learned Hash Function to map and retrieve data across a distributed system while preserving the inherent order of data. LEAD includes the designs to minimize range query latency and message cost while maintaining high scalability and resilience to network churn. Our comprehensive evaluations, conducted in both testbed implementation and simulations, demonstrate that LEAD achieves tremendous advantages in system efficiency compared to existing range query methods in large-scale distributed systems, reducing query latency and message cost by 80% to 90%+. Furthermore, LEAD exhibits scalability and robustness against system churn, providing a robust, scalable structure for efficient data retrieval in distributed key-value systems.

Blockchain interoperability and throughput scalability are two crucial problems that limit the wide adoption of blockchain applications. Payment channel networks (PCNs) provide a promising solution to the inherent scalability problem of blockchain technologies, allowing off-chain payments between senders and receivers via multi-hop payment paths. This paper presents a cross-chain PCN, called XHub, that extends PCNs to support multi-hop paths across multiple blockchains and resolves both interoperability and throughput scalability. XHub achieves service availability, transaction atomicity, and auditability. Users who correctly follow the protocols will succeed in making payments or get profits from doing the services. In addition, trustworthy information about hubs will be managed in a decentralized manner and available to all users. We conduct prototype implementation of machines that exchange Internet messages and run with two real blockchains as well as large-scale simulations based on real-world PCN topologies and transactions. The results show that XHub has small latency for cross-chain payments and can achieve a significantly higher success rate compared to the version without hub management protocols. This work is an important step towards the big picture of a decentralized transaction system that connects a wide scope of users in different blockchains.