Large Language Models (LLMs) deployed on mobile devices offer benefits like user privacy and reduced network latency, but introduce a significant security risk: the leakage of proprietary models to end users.

Intrusion Detection Systems (IDSes) are widely employed to identify potential attacks in guest virtual machines (VMs). Nonetheless, traditional IDSes fall short of the demands of high-performance clouds. First, monitoring VM events increases the tail latency of guest services. Second, the throughput of traditional IDSes cannot meet high-performance cloud requirements, leading to event loss and reduced detection accuracy. Finally, cloud providers typically run complex IDS tools within the VM. Updating IDS functionality requires modifying guest VMs, which hurts maintainability. To overcome these challenges, this article presents EIDS, a cloud IDS framework with high performance and good maintainability. We observe that the main bottleneck is collecting VM status, and the collected status can be divided into fundamental and supplementary status. EIDS then splits the status collection procedure spatially and temporally. First, we provide a status monitor with a separate architecture that isolates the status collection logic in a microVM, thus minimizing the code in guest VMs and improving maintainability. Second, EIDS introduces a two-phase status collection method to handle multiple events in batches, asynchronously, for high IDS throughput. A tiny tracer, implemented with eBPF, operates inside the user VM to collect fundamental status. The complex status collector runs in an isolated microVM. It utilizes Virtual Machine Introspection (VMI) to gather supplementary status, using the fundamental status to bridge the semantic gap. The status collector batches the collection for multiple events to amortize the fixed overhead of microVM switching and improve event tracing throughput. Finally, to minimize tail latency overhead, a fine-grained and workload-aware scheduler executes IDS logic with small time slices during user VM idle periods. We implemented a prototype of EIDS in Linux-KVM and conducted a comprehensive evaluation. We compared EIDS’s performance with Falco, an open-source IDS widely used by Kubernetes and AWS for runtime security monitoring. The results demonstrate that, compared to Falco, EIDS reduces the 99 th -percentile latency overhead by 97% and achieves a 13.8X improvement in IDS event handling throughput.

VM-level containers provide strong isolation by running each container with its own kernel in a VM. However, they rely on virtualization hardware designed for general-purpose VMs, causing non-negligible performance overhead compared to OS-level containers. This performance gap widens dramatically in nested virtualization scenarios, where secure containers run inside a VM.

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 highperformance decentralized computing system that maintains wholelifecycle 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 116× and 2.2 * 10 7 × latency speedup for database and machine learning applications, respectively, compared with Ethereum.

Consortium decentralized systems, also known as consortium systems, enable consensus and availability among limited untrusted participants.Given the growing necessity for inter-organizational data sharing, consortium systems have gained significant prominence in cross-enterprise collaboration.File systems, which play a fundamental role in data sharing, face new challenges within consortium systems.The consortium system involves characteristics of both decentralized and centralized systems.The first requirement is decentralization.The system's functionality, availability, and security must not depend on any individual node.The second requirement is characteristic-awareness, which necessitates optimal data placement across nodes based on policy constraints, performance requirements, and security considerations.The third requirement is high performance and flexible access control.Neither centralized nor existing decentralized file systems can satisfy all three requirements.This paper presents DeFS, a novel decentralized file system designed for consortium systems.DeFS implements a two-layer architecture that incorporates public nodes into the consortium system, thereby enhancing decentralization.Additionally, we propose a Multi-Ring Distributed Hash Table (MR-DHT) protocol to facilitate characteristic-aware data block distribution.To optimize data routing efficiency, we introduce the Location Cache (L-Cache) mechanism.We have implemented a DeFS prototype and conducted comprehensive performance evaluations across three distinct network configurations, with the largest one having over 1,500 nodes.Results show that DeFS successfully achieves characteristic-aware data placement while delivering 10.32X lower latency compared to IPFS on average.

Integrated sensing and communication (ISAC) sys-tems have attracted considerable attention in recent years. This paper proposes an ISAC system based on orthogonal time frequency space (OTFS) modulation, designed to enhance performance in high-mobility environments. We introduce a novel passive sensing method that enables high-resolution target parameter estimation through fine-grained grid search, two-stage parameter refinement. Additionally, we develop a channel estimation method that leverages sensing parameters, utilizing delay-Doppler domain information in OTFS systems to enhance accuracy in high-mobility scenarios. Simulation results across various Signal-to-Noise ratio (SNR) conditions demonstrate the effectiveness of the proposed methods, showing a significant reduction in the root mean square error (RMSE) of distance and velocity estimations as SNR increases. These findings highlight the accuracy of the proposed algorithms in high-noise environments.

Vehicular Edge Computing (VEC) offers a promising framework for providing vehicles with low-latency and highly reliable services. By leveraging the underutilized computational resources of parked and moving vehicles commonly found in urban areas, a VEC system can enhance the performance of surrounding user devices and alleviate the loads on its edge servers. In this study, a resource orchestration scheme is introduced for a multi-device, multi-vehicle, and multi-edge scenario. Tasks from a device can be offloaded to its associated edge server, a neighboring edge server, a parked vehicle, or a moving vehicle. Our goal is to achieve the total task processing cost (comprising task processing latency and energy consumption) minimization across all devices through making strategies for task offloading and computational and communication resource allocation. We decompose the optimization problem and propose a Twin Delayed Deep Deterministic Policy Gradient (TD3)-based Deep Reinforcement Learning (DRL) algorithm. Furthermore, to accelerate the convergence speed of the algorithm, we optimize the uplink transmit power allocation sub-problem separately by designing a numerical algorithm. We analyze the complexity of the algorithm and assess its convergence. Through extensive simulations across 5 different scenarios, our proposed scheme outperforms 4 reference schemes, showcasing reductions in total task processing costs ranging from 15.13% to 38.59%.

Vehicular edge intelligence, distinct from traditional edge intelligence, exhibits unique characteristics, including the mobility of vehicles, uneven spatial and temporal distribution of vehicles, and variability in the AI models deployed on vehicles, Roadside Units (RSUs), and edge servers (ESs). In this paper, we propose a Deep Reinforcement Learning (DRL)-based resource orchestration scheme for task inference in vehicle-RSU-edge collaborative networks. In our approach, vehicles' inference tasks can be processed on the vehicles, RSUs, or ESs, encompassing a total of 9 possible scenarios based on the cross-RSU mobility of vehicles. The scheme jointly optimizes task processing decision-making, transmission power allocation, computational resource allocation, and transmission rate allocation. The objective is to minimize the total cost, which involves a trade-off between task processing latency, energy consumption and inference error rate across all vehicle tasks. We design a DRL algorithm that decomposes the original optimization problem into sub-problems and efficiently solves them by combining the Softmax Deep Double Deterministic Policy Gradients (SD3) algorithm with multiple numerical methods. We analyzed the complexity and convergence of the algorithm. Specifically, we demonstrated its low complexity and fast, stable convergence, which prove its effectiveness in solving the problem. And we demonstrate the superiority of our scheme by comparing it with 5 benchmark schemes across 6 different scenarios.

The code base of system software is growing fast, which results in a large number of vulnerabilities: for example, 296 CVEs have been found in Xen hypervisor and 2195 CVEs in Linux kernel. To reduce the reliance on the trust of system software, many researchers try to provide trusted execution environments (TEEs), which can be categorized into two types: non-privileged TEEs and privileged TEEs. Non-privileged TEEs (e.g., Intel SGX) are extensible, but cannot protect security services like virtual machine introspection (VMI) due to the lack of system-level semantics. On the contrary, privileged TEEs (e.g., the secure world of ARM TrustZone) have system-level semantics, but any additional service implemented in the privileged TEE directly increases the TCB of the entire system. In this article, we propose a new design of TEE to support system-level security services and achieve better extensibility with a small TCB. Each TEE instance of the proposed design is named aColony. Specifically, we introduce asecure monitorfor isolation and capability management. EachColonyis assigned capabilities to access only necessary system-level semantics. We use the new TEE to build four security services, including secure device accessing, VMI tools, a system call tracer, and a much more complex service to virtualize ARM TrustZone with multipleColonies. We have implemented the system on ARMv7 and ARMv8 platforms, in Xen hypervisor and Linux kernel, and perform a detailed evaluation to show its efficiency.11.This paper is an extended version of the conference paper published in USENIX Security’17: vTZ: Virtualizing ARM TrustZone[29]. A brief summary of differences is in Section8.

Garbage collection (GC), especially full GC, would non- trivially impact overall application performance, especially for those memory-hungry ones handling large data sets. This paper presents an in-depth performance analysis on the full GC performance of Parallel Scavenge (PS), a state-of-the-art and the default garbage collector in the HotSpot JVM, using traditional and big-data applications running atop JVM on CPU (e.g., Intel Xeon) and many-integrated cores (e.g., Intel Xeon i). The analysis uncovers that unnecessary memory accesses and calculations during reference updating in the compaction ase are the main causes of lengthy full GC. To this end, this paper describes an incremental query model for reference calculation, which is further embodied with three schemes (namely optimistic, sort-based and region-based) for different query patterns. Performance evaluation shows that the incremental query model leads to averagely 1.9X (up to 2.9X) in full GC and 19.3% (up to 57.2%) improvement in application throughput, as well as 31.2% reduction in pause time over the vanilla PS collector on CPU, and the numbers are 2.1X (up to 3.4X), 11.1% (up to 41.2%) and 34.9% for Xeon i accordingly.