Machine learning and data analysis algorithms leverage massive datasets to deliver powerful functionalities. However, these datasets are often distributed across multiple parties and individual users. Decentralized private computing systems enable data consumers to execute algorithms on third-party data in a secure manner, eliminating the need for trust in any single node. Nevertheless, existing systems, primarily derived from blockchain technology, target small-scale workloads. Sharing large-scale datasets presents new challenges. First is data-oriented access control, enabling data providers to enforce flexible access policies throughout the complex utilization of their datasets. Second is high performance, which is essential for data analysis applications involving large-scale datasets and sophisticated computational logic. Third is whole-lifecycle privacy, ensuring comprehensive protection of both data and algorithmic privacy from task initiation through result delivery. To address these challenges, we present DPCapsule, a high-performance decentralized computing system that maintains whole-lifecycle privacy. DPCapsule introduces a novel data abstraction called Capsule, which encapsulates dataset and access policies within a trusted execution environment (TEE)-based shell, enabling data providers to control their datasets throughout subsequent computations. A Capsule reborn mechanism is provided for automated access policy updates. Additionally, we design a two-layer execution architecture and consensus protocol to facilitate private computation with scalable performance. Furthermore, a secure execution protocol is designed to guarantee whole-lifecycle privacy for both dataset, algorithms, and metadata. We have developed a prototype of DPCapsule and conducted evaluations across various configurations, with networks scaling up to 32 nodes. Experimental results show that DPCapsule effectively scales to 32 nodes, achieving 116x and 2.2 * 10(7) x latency speedup for database and machine learning applications, respectively, compared with Ethereum.
Software development is currently under a paradigm shift in which artificial intelligence and generative software reuse are taking the center stage in software creation. Consequently, earlier software reuse practices and methods are rapidly being replaced by AI-assisted approaches in which developers place their trust on code that has been generated by artificial intelligence. This is leading to a new form of software reuse that is conceptually not all that different from cargo cult development. In this short paper we discuss the implications of AI-assisted generative software reuse in the context of emerging "AI native" software engineering, bring forth relevant questions, and define a tentative research agenda and call to action for tackling some of the central issues associated with this approach.
Satellite-based cloud computing cybersecurity threats have long posed significant challenges, particularly for cloud infrastructure operators. While prior research has partially addressed these issues by mitigating threats and enhancing human response efficiency, this paper proposes a novel AI-Driven Threat Analysis and Response (TAR) framework. The study progresses in three main phases: (1) redefining urgent threats through a novel formula; (2) implementing a triage and analysis framework using augmented Large Language Models (LLMs); and (3) automating incident response via a Security Orchestration, Automation, and Response (SOAR) platform. Our prototype, tested in a simulated public cloud environments using real production threats, demonstrated a 17% improvement in handling low- and medium-urgency threats. Experimental results show our approach achieves 97.8% coverage in automatic threat classification, significantly outperforming traditional manual methods, which achieve 77.8% coverage. With high recall and precision in managing low- and medium-urgency threats, our method enhances manual efficiency through SOAR-enabled automation. Furthermore, our augmented method surpasses the state-of-the-art GPT-4 Turbo model in addressing security threats containing Chinese characters.
localization method that has been extensively studied in recent years. The intuition behind MBFL is based on the assumption that mutation operations can correct faults in a program. However, this assumption has only been experimented and validated on simulated datasets, and whether it truly holds in the real world has never been investigated. Fault types in simulated datasets are simple and differ significantly from the complex and diverse faults found in real-world programs. Therefore, to investigate whether MBFL works in the real world, it is necessary to validate its intuition in the real world. The goal of this study is to analyze whether the intuition of MBFL still holds in the real world. We quantified the MBFL intuition by establishing an algorithm, which eliminated the interference of factors unrelated to MBFL itself, allowing us to directly validate the intuition of MBFL. Based on this algorithm, we conducted extensive experiments on both real-world programs and programs in simulated datasets. The results revealed an interesting trend: due to the complexity of faults in real-world programs compared to those in simulated datasets, MBFL's intuition probably cannot hold in the real world. This indicates that MBFL's intuition is difficult to hold in the real world. Consequently, we focused on analyzing the real-world faulty versions and summarized a set of mutation operators that perform better in the real world by studying the types and effects of each mutant, providing guidance for the application of MBFL.
Due to the rapid development of deep neural networks (DNNs), ensuring their quality has become increasingly important. However, the test oracle problem poses an obstacle to DNN testing because of the massive unlabeled data. Metamorphic Testing (MT) has proven effective in alleviating the test oracle problem, and many efforts have been made to improve the cost-effectiveness of MT for DNNs. Some approaches focus on selecting good metamorphic relations (MRs), while others target the selection of suspicious source test cases. Since follow-up test cases are generated by combining source test cases with MRs, selecting effective pairs of source test cases and MRs is also quite essential and beneficial for MT. In this paper, we propose CMPS, a multi-objective black-box approach for metamorphic test case pair selection. Considering both uncertainty and diversity, CMPS aims to select pairs that can detect more unique faults in the model. It evaluates uncertainty based on model outputs and assesses diversity through clustering source test cases. Furthermore, CMPS can adaptively optimize the selection process based on feedback from the execution results of the selected pairs. We conduct extensive experiments on three datasets and five DNN models to evaluate CMPS's performance. The experimental results demonstrate that CMPS significantly outperforms baseline approaches in both failure triggering and fault detection.
