With the advancement of artificial intelligence and UAV technologies, UAV swarms have been increasingly applied in a wide range of diversified missions. However, existing studies remain limited in their characterization of UAV swarm under dynamic mission demands and multi-layer complex interactions, as well as in the quantitative evaluation of their reliability. To address these gaps, this study proposes a mission-oriented multi-layer super-network modeling and reliability evaluation framework. First, four heterogeneous sub-networks are constructed from the dimensions of operation, mission, communication, and resource, capturing the swarm’s multi-layer interaction relationships. Then, considering inter-layer dependencies and cascading failures, a random failure and network reconfiguration strategy is introduced to quantitatively analyze the impact of key node failures on topological metrics and network vulnerability. Finally, a fire rescue case study is conducted to verify the effectiveness and accuracy of the proposed method.

Graph data analysis, particularly local triangle counting, plays a pivotal role in deciphering complex relationships within graph data. This method is invaluable across diverse fields such as social networks, transportation, and cybersecurity. However, this process often involves handling sensitive information, necessitating that the relationship between any two nodes is considered private. Differential privacy (DP) is a formal model to address privacy concerns and can be categorized into two types: the central DP (CDP) model, which achieves better result accuracy, and the local DP (LDP) model, which does not assume a trusted server. To bridge the gap between the two models, we propose Sectric, a server-aided crypto-assisted local triangle counting protocol, in this paper. It can achieve the same result accuracy with the same privacy budget as the CDP model without assuming a trusted server. Sectric also explores a new approach in crypto-assisted graph data analysis algorithms that represents a node's neighbors using a set instead of an adjacency vector, and successfully achieves higher efficiency compared to other crypto-assisted solutions. We also conduct theoretical and empirical evaluations to demonstrate that Sectric achieves the design principles.

The Payment Channel Network (PCN) stands out as one of the most promising technologies for scaling blockchain-based cryptocurrencies. However, a noteworthy challenge arises during the utilization of PCNs, where a substantial portion of payment channels gradually becomes exhausted, leading to a reduction in the overall availability of PCNs. This issue is crucial in the context of blockchain off-chain PCNs and warrants a comprehensive investigation. In this paper, we introduce the problem of optimal recover and propose OPtimal REcovering protocols, denoted asOPREandOPRE+, to address this challenge. The protocols target at recovering the optimal number of nearly exhausted channels in the PCN. OPRE provides a basic solution, and OPRE+ is an augmentation which provides a more efficient and effective solution. Furthermore, to address users’ privacy concerns, we propose privacy-preserving versions of the protocols, ensuring that users’ balance on payment channels remains undisclosed during the execution of the protocols. Beyond the theoretical design and analysis, we implement these protocols and conduct experimental evaluations to assess their performance. The results affirm that our protocols exhibit efficiency and effectiveness in significantly improving the availability of PCNs.

Recently, watchtowers emerge as a critical service within payment channel networks (PCNs). Existing payment channels necessitate that channel owners periodically monitor the blockchain to ensure their fund security, or enlist the watchtower services for this task. Presently, watchtower proposals mandate the implementer to provide collateral as a safeguard against its collusion with potential adversaries. However, this collateral substantially inflates the implementation costs, consequently leading to higher service fees for users. Furthermore, most watchtowers are typically operated by a third-party entity, creating a single point of failure. To ameliorate the status quo, we propose a novel approach where PCN nodes collaboratively implement a watchtower system named “SilenTower.” Our proposal is rooted in the fundamental principles of blockchain systems, emphasizing maintenance by a community with an honest majority. SilenTower’s security no longer relies on collateral but rather on the inherent difficulty of a large proportion of collusion. SilenTower also allows inaccessible participants and thus obtains robustness. Through a rigorous theoretical analysis, we demonstrate that participants’ optimal strategy is to remain accessible and faithfully adhere to the SilenTower protocol. Furthermore, we assess the practical performance of SilenTower through comprehensive benchmarking, and the results reveal that it introduces lightweight overheads.

Payment channels emerge as a promising solution to the scalability issues of blockchain-based digital currency systems, but they implicitly assume that channel owners can periodically monitor the blockchain, which may be impractical for most ordinary users. To address this issue, watchtowers are developed to monitor the blockchain on behalf of its hirers, allowing them to stay offline without security concerns. Despite their usefulness, current watchtower implementations face a security and scalability dilemma. They either require hirers to trust watchtowers, making hirers' funds vulnerable if the watchtower colludes with the counterparty, or require the watchtower to deposit collateral for each hirer, restricting the service scalability due to the watchtower's limited funds. To overcome this dilemma, we propose SilenTower, a mul-tiparty watchtower protocol. It allows a given proportion of collusive protocol participants and provides fund security without collateral. Thus, SilenTower achieves security and scalability simultaneously. Moreover, we propose a quantified definition for watchtower robustness and prove that SilenTower has better robustness than state-of-the-art implementations. We also assess SilenTower's performance through thorough benchmarking and demonstrate that it has lightweight overheads for both partici-pants and hirers.

Redundancy elimination (RE) systems allow network users to remove duplicate parts in their messages by introducing caches at both message senders’ and receivers’ sides. While RE systems have been successfully deployed for handling unencrypted traffic, making them work over encrypted links is still open. A few solutions have been proposed recently, however they either completely violate end-to-end security or focus on single-user setting. In this paper, we present a highly secure RE solution which supports cross-user redundancy eliminations on encrypted traffics. Our solution not only preserves the end-to-end security against outside adversaries, but also protects users’ privacy against semi-honest RE agents. Furthermore, our solution can defend malicious users’ poisoning attack, which is crucial for cross-user RE systems but has never been studied before. In cross-user RE systems, since all users inside a LAN write into a shared, global cache and use it to recover their original messages from deduplicated ones, the poisoning attack is prone to happen, and cause systematic damage to all users even when only one user is malicious and injects poisoned data into the cache. We rigorously prove our solution’s security properties, and demonstrate its promising performance via testing the proof-of-concept implementation with real-world internet traffic data.

Payment Channel Network (PCN) is one of the most promising technologies for scaling the capacity of blockchain-based cryptocurrencies and improving the quality of blockchain-based services. However, during the use of PCNs, a significant portion of the payment channels gradually become exhausted, which triggers additional consumption of on-chain resources and makes PCNs less useful. This is a fundamental problem for blockchain-based cryptocurrencies, worthy of a thorough investigation.In this paper, we propose OPRE, a protocol for OPtimal off-chain REcovering of payment channels, to solve this problem. It is optimal in that it recovers the maximum number of nearly exhausted channels in the PCN. Furthermore, we consider users’ privacy concerns and design a privacy-preserving version of this protocol, so that users’ balance information does not need to be revealed. This protocol maintains optimality in recovering payment channels while providing cryptographically strong privacy guarantee. In addition to the theoretical design and analysis, we also implement OPRE and experimentally evaluate its performance. The results show that the OPRE protocol is both efficient and effective.

As one of a few critical technologies to cloud storage service, deduplication allows cloud servers to save storage space by deleting redundant file copies. However, it often leaks side channel information regarding whether an uploading file gets deduplicated or not. Exploiting this information, adversaries can easily launch a template side-channel attack and severely harm cloud users' privacy. To thwart this kind of attack, we resort to the k-anonymity privacy concept to design secure threshold deduplication protocols. Specifically, we have devised a novel cryptographic primitive called “dispersed convergent encryption” (DCE) scheme, and proposed two different constructions of it. With these DCE schemes, we successfully construct secure threshold deduplication protocols that do not rely on any trusted third party. Our protocols not only support confidentiality protections and ownership verifications, but also enjoy formal security guarantee against template side-channel attacks even when the cloud server could be a “covert adversary” who may violate the predefined threshold and perform deduplication covertly. Experimental evaluations show our protocols enjoy very good performance in practice.