The use of healthcare data for collaborative machine learning training amplifies the demand for strong privacy and security protection. Federated learning (FL) addresses this by sharing model gradients instead of raw patient data. However, FL faces critical challenges, including vulnerability to adversarial attacks (e.g., membership inference, model poisoning), reliance on centralized aggregation (introducing single points of failure), and privacy leakage risks from gradient exchanges. To overcome these limitations, we propose C2SecFL, a cross-clustered secure FL framework that combines adaptive, model-aware clustering with an inner-product functional-encryption (IPFE) scheme enabling encrypted aggregation without a key distribution center (KDC). A lightweight permissioned blockchain provides tamper-evident coordination and auditability across clusters without introducing a centralized trust anchor. Experiments on diverse models show substantially lower cryptographic overhead compared to homomorphic-encryption baselines. Encryption is approximately 3.6× faster, and aggregation is up to 2.2× faster, while predictive utility is preserved. Under adversarial settings with compromised participants, C2SecFL reduces the average attack success rate by about 80% relative to a FedAvg baseline. It mitigates membership-inference risk by encrypting updates end-to-end and revealing only aggregate inner products. Our extensive experiments on a heart attack dataset demonstrate that C2SecFL provides a practical, auditable, and resilient approach to secure healthcare FL.

The expansion in the scale of collaborative scientific efforts has brought about numerous challenges in continuous integration/deployment (CI/CD). Current CI/CD tools solely depend on centralized data management, resulting in reliability and scalability concerns. To address the issue, this paper advocates for and demonstrates a decentralized approach that leverages lightweight data management techniques and distributed computing principles. Our middleware solution, DeQL, transforms the current CI/CD workflows, and uses decentralized logic for automated code analysis before GitHub commits. The involvement of distributed participants (i.e., GitHub contributors), together as a part of the decentralized network, ensures code integrity, security and reliable integration processes. For ensuring low overheads and scalability, this paper introduces lightweight consensus protocols designed for efficient smart contract execution, significantly reducing overhead compared to traditional decentralized data management solutions. We incorporate a fine-grained CI/CD commit dependency graph that effectively manages CI/CD commit complexity, facilitating faster (and more reliable) processing. Experimental results show that the proposed decentralized protocol outperforms the traditional centralized CI/CD system and state-of-the-art blockchain protocols, achieving up to 80% faster response times and handling 7× more commits in a given amount of time.

Ensuring data privacy in blockchain systems remains challenging due to the heavy computational and communication costs of traditional cryptographic mechanisms. Existing solutions often suffer from limited scalability, high resource consumption, and inefficient tamper-proof key management. To address these challenges, we propose Zero Trust Privacy (ZTP), a lightweight framework for scalable on-chain privacy and secure distributed key management. ZTP introduces a hybrid quorum protocol using dynamic scale-free graph adjustments and a parallel data and key management mechanism based on the Geometric Fragmentation Technique (GFT), achieving efficient, tamper-resistant shard handling. To further enhance scalability, we incorporate a lightweight consensus protocol with parallel transaction processing, isolating transactions and key access from untrusted blockchain nodes. We implement and evaluate ZTP on a distributed blockchain prototype, demonstrating outstanding performance, achieving up to 49% fault tolerance, and delivering speedups of at least 55 × compared to state-of-the-art blockchain protocols. Our results highlight ZTP’s potential for resource-constrained and large-scale blockchain deployments.

The healthcare sector consistently handles vast amounts of sensitive data, which must always be kept secure and private. Unlike classical machine learning (ML) models that require centralizing all data, federated learning (FL) addresses privacy concerns by enabling collaborative learning without sharing raw data. However, FL models are subject to limitations when managing diverse datasets from pervasive sources and encounter challenges such as adversarial threats; in addition, the generalizability of the resulting global models may limit the effectiveness of the analysis. This paper presents a novel decentralized and cluster-based FL framework designed to enhance healthcare data privacy and strengthen the security of FL processes. This framework addresses vulnerabilities by decentralizing the FL models. It includes a weighted voting mechanism that aims to improve analytics accuracy by aggregating decisions from multiple clusters. Additionally, the proposed approach reduces the risk of adversarial attacks by employing both cluster-based strategies and feature-squeezing (FS). Experimental results show that our approach surpasses classical FL methods in accuracy and security at various stages of the FL process.

Edge computing draws a lot of recent research interests because of the performance improvement by offloading many workloads from the remote data center to nearby edge nodes. Nonetheless, one open challenge of this emerging paradigm lies in the potential security issues on edge nodes. This paper proposes a cooperative protocol, namely DEAN, equipped with a unique resource-efficient quorum building mechanism to adopt blockchain seamlessly in an edge computing infrastructure to prevent data manipulation and allow fair data sharing with quick recovery under resource constraints of limited storage, computing, and network capacity. Specifically, DEAN leverages a parallel mechanism equipped with three independent core components, effectively achieving low resource consumption while allowing secured parallel block processing on edge nodes. We have implemented a system prototype based on DEAN and experimentally verified its effectiveness with a comparison with four popular blockchain implementations: Ethereum, Parity, IOTA, and Hyperledger Fabric. Experimental results show that the system prototype exhibits high resilience to arbitrary failures. Performance-wise, DEAN-based blockchain implementation out-performs the state-of-the-art blockchain systems with up to 88.6 x higher throughput and 26 x lower latency.

The state-of-the-art for auditing and reproducing scientific applications on high-performance computing (HPC) systems is through a data provenance subsystem. While recent advances in data provenance lie in reducing the performance overhead and improving the user's query flexibility, the fidelity of data provenance is often overlooked: there is no such way to ensure that the provenance data itself has not been fabricated or falsified. This paper advocates leveraging blockchains to deliver immutable and autonomous data provenance services such that scientific discoveries are trustworthy. The challenges for adopting blockchains to HPC include designing a new blockchain architecture compatible with the HPC platforms and, more importantly, a set of new consensus protocols for scientific applications atop blockchains. To this end, we have designed the proof-of-scalable-traceability (POST) protocol and implemented it in a blockchain prototype, namely SciChain, the very first practical blockchain system for provenance services on HPC. We evaluated SciChain by comparing it with multiple state-of-the-art systems; experimental results showed that SciChain guaranteed trustworthy data provenance while incurring orders of magnitude lower overhead than existing solutions.

Distributed resiliency becomes paramount to alleviate the growing costs of data movement and I/Os while preserving the data accuracy in HPC systems. This paper proposes to adopt blockchain-like decentralized protocols to achieve such distributed resiliency. The key challenge for such an adoption lies in the mismatch between blockchain's targeting systems (e.g., shared-nothing, loosely-coupled, TCP/IP stack) and HPC's unique design on storage subsystems, resource allocation, and programming models. We present BAASH, Blockchain-As-A-Service for HPC, deployable in a plug-n-play fashion. BAASH bridges the HPC-blockchain gap with two key components: (i) Lightweight consensus protocols for the HPC's shared-storage architecture, (ii) A new fault-tolerant mechanism compensating for the MPI to guarantee the distributed resiliency. We have implemented a prototype system and evaluated it with more than two million transactions on a 500-core HPC cluster. Results show that the prototype of the proposed techniques significantly outperforms vanilla blockchain systems and exhibits strong reliability with MPI.

We present Reflector, to support both high-level and low-level I/O monitoring through user-defined interfaces such as HDF5 and NetCDF in addition to POSIX- and MPI-IO. We evaluate Reflector on both an on-premises 500-core HPC cluster and a leadership-class supercomputer at the Lawrence Berkeley National Laboratory. Preliminary results are promising as the system prototype incurs negligible performance overhead and clearly illustrates the I/O patterns and bottlenecks of multiple applications.

The state-of-the-art approaches for tracking data provenance on high-performance computing (HPC) systems are either supported by file systems or relational databases. These techniques shared the same critique on the provenance data’s fidelity and the associated I/O overhead. This paper envisions to track the HPC data provenance using a distributed in-memory ledger—the core technique leveraged by blockchains and proven to be highly trustworthy by many large-scale applications. We pinpoint two system challenges—storage architecture and consensus protocol—for adopting blockchains to HPC and make the following contributions: (i) We design a new in-memory blockchain architecture for HPC systems, exploiting the high-performance network infrastructure InfiniBand and greatly reducing the I/O overhead; and (ii) We develop a new consensus protocol, namely proof-of-reproducibility (PoR), crafted for the new architecture, which takes into account both proof-of-work (PoW) and proof-of-stake (PoS) mechanisms. The correctness of PoR is both theoretically proven and experimentally verified. A prototype system is implemented and evaluated with more than one million transactions, showing 32× speedup compared to the filesystem-based provenance service and four orders of magnitude speedup compared to the database-based provenance service.

Many modern scientific applications involve large volumes of multidimensional data and extensive computation. Although distributed systems and tools are becoming increasingly scalable, they are still far away to catch up the exponential growth rate exhibited by many of those scientific big-data applications. This paper presents our early effort on overcoming the exponential complexity of one widely deployed workload over multidimensional scientific data—the n×n numerical analysis on two-dimensional arrays. More specifically, we propose a new approach to reduce the exponentially-grown data into a semantically-equivalent polynomial form in the context of two-dimensional electrode arrays, which are widely used in biomedical engineering, electrical engineering, and mechanical engineering. We have implemented a system prototype in Python, preliminary results show that the proposed approach outperforms the state-of-the-practice in various metrics: (i) the consumed space is six orders of magnitude smaller; (ii) the execution time is three orders of magnitude faster; and (iii) the scalability is improved by two orders of magnitude—from 6×6 to 100 × 100—on mainstream servers in reasonable time.

In a wireless sensor network (WSN) where nodes are mostly battery-powered, queries' energy consumption and response time are two of the most important metrics as they represent the network's sustainability and performance, respectively. Conventional techniques used to focus only one of the two metrics and did not attempt to optimize both in a coordinated manner. This work aims to achieve both high sustainability and high performance of WSN queries at the same time. To that end, a new mechanism is proposed to construct the topology of a three-tier WSN. The proposed mechanism eliminates routing tables and employs a novel and efficient addressing scheme inspired by the Chinese Remainder Theorem (CRT). The CRT-based topology allows for query parallelism, an unprecedented feature in WSNs. On top of the new topology encoded by CRT, a new protocol is designed to parallelly preprocess collected data on sensor nodes by effectively aggregating and deduplicating data in a neighborhood cluster. Moreover, a new algorithm is devised to allow the queries and results to be transmitted through low-power and fault-tolerant paths using recursive elections over a subset of the entire power range. With all these new techniques combined, the proposed system outperforms the state-of-the-art from various perspectives: (i) the query response is improved by up to 53%; (ii) the energy consumption is reduced by up to 70%; and (iii) the reliability is increased by up to 39%.