Millimeter-wave (mmWave) imaging is a key sensing modality for cyber-physical systems (CPS), enabling applications in autonomous robotics, industrial inspection, and security screening. Yet collecting large, labeled mmWave imaging datasets is costly, limiting progress in robust perception. We present MilliSARImageNet, a high-resolution mmWave imaging dataset that couples a physics-based digital-twin simulation with real measurements from a 77–81 GHz imager. The digital twin replicates the real imager’s geometry, waveform, and reconstruction pipeline, enabling scalable, physically consistent data generation, while the real subset provides an evaluation reference for synthetic-to-real transfer. Images are reconstructed using four imaging algorithms, and targeted augmentations are applied to capture realistic variability. Fine-tuning ConvNeXt-B, Swin-B, ViT-B/16, and ResNet-152 solely on synthetic images yields 95%–98% classification accuracy on unseen synthetic images and 81%–88% on unseen real images. These results show that our digital-twin approach is effective while still revealing a remaining synthetic-to-real domain gap. MilliSARImageNet provides a reproducible foundation for studying domain adaptation and robust, trustworthy mmWave perception in CPS.

Standard multiparty computation (MPC) protocols in dishonest-majority settings lack mechanisms to identify malicious actors or preserve computational progress, leaving them vulnerable to denial-of-service attacks in real-world deployments. Adversaries can force premature termination, resulting in wasted computational resources and significant financial losses. To address this challenge, we propose a comprehensive solution that integrates detection and recovery through three components: (1) a block-based computation model that decomposes monolithic MPC protocols into verifiable units for easy recovery; (2) lightweight cryptographic "canary values" that help detect and attribute malicious behavior without compromising privacy; and (3) a game-theoretic framework that creates economic incentives for honest protocol participation. Experimental evaluations demonstrate a reduction of up to 56.65% in recovery time compared to full restarts, with a computational overhead of 4.5%-11.8% depending on the configuration. The solution offers a practical trade-off between security and performance, thereby improving the viability of MPC in malicious cloud environments.

As edge computing and the Internet of Things (IoT) expand, horizontal collaboration (HC) emerges as a distributed data processing solution for resource-constrained devices. In particular, a convolutional neural network (CNN) model can be deployed on multiple IoT devices, allowing distributed inference execution for image recognition while ensuring model and data privacy. Yet, this distributed architecture remains vulnerable to adversaries who want to make subtle alterations that impact the model, even if they lack access to the entire model. Such vulnerabilities can have severe implications for various sectors, including healthcare, military, and autonomous systems. However, security solutions for these vulnerabilities have not been explored. This paper presents a novel framework for Secure Horizontal Edge with Adversarial Threat Handling (SHEATH) to detect adversarial noise and eliminate its effect on CNN inference by recovering the original feature maps. Specifically, SHEATH aims to address vulnerabilities without requiring complete knowledge of the CNN model in HC edge architectures based on sequential partitioning. It ensures data and model integrity, offering security against adversarial noise in diverse HC environments. Our evaluations demonstrate SHEATH’s adaptability and effectiveness across diverse CNN configurations.

Radio frequency (RF) sensing and imaging systems play important roles today, from remote sensing to airport passenger screening to medical imaging. Nevertheless, the security of these systems has not been studied sufficiently. Existing attack methods under consideration are mostly jamming, interfering, etc. Because such attacks can easily be detected by the systems and thus be avoided, it has given a false sense of security in applying the sensing systems. This paper shows a more evasive attack called camouflage attack that makes the sensing systems generate false but normal-looking images and can evade the detection of the sensing systems. The proposed camouflage attack algorithm is conducted by a wireless transmitter to broadcast a signal designed according to the knowledge of the sensing system or according to the intercepted signals. Extensive simulations and experiments are conducted to verify the validity of this new attack. Especially, this attack is shown as transferable among different sensing/imaging algorithms and robust to sensor location ambiguities. The aim of this work is to encourage and motivate further research to strengthen the security of RF sensing systems.

Machine Learning (ML) applications are prominent in many fields due to their ability to derive insights and automate processes. Many services utilize data from smart devices to offer personalized services to users. However, privacy concerns arise when sensitive data is collected by such devices for ML operations and outsourced to the cloud, along with the high liability costs associated with a security breach. Multiparty computation (MPC) is a promising method for privacy-preserving ML. Nonetheless, it has a significant computational overhead, mainly due to the large number of associated Matrix Multiplications (MM) in ML training and inference. This paper improves the arithmetic complexity of secure MM over MPC using the Strassen algorithm. We formulated a Multi-Objective Optimization Problem (MOOP) to optimize the trade-off among resource costs, execution time of secure MM, and the potential security loss due to a cyberattack. We implemented and analyzed the performance of several classical solutions to the MOOP, including Brute Force and the Non-Dominated Sorting Genetic Algorithm (NSGA). Using insights from these evaluations, we developed a solution, DepthSwift, which judiciously, efficiently, and quickly solves the MOOP for all required MMs in ML training or inference. Our implementation over SPDZ shows that we can significantly reduce resource costs and minimize potential security loss with respect to the naive MM over MPC.

Although Collaborative Deep Neural Network (CDNN) promises to be an alternative mechanism to mitigate the effects of the untrusted cloud, this approach is susceptible to other kinds of adversarial attacks, which arise from one or more untrusted devices in CDNN acting maliciously. However, since each untrusted node in the CDNN contains only partial information of the complete DNN model, it is worth investigating whether the attacker can still muster a viable threat to CDNN or not. This led to the investigation of attack scenarios and their effects on convolutional neural networks (CNN) used for image classification in the CDNN environment. In this research, we are investigating the shortcomings of existing attacks on CDNN, that lead to non-subtle attack to the defender who is on look out against such attacks. Our research showed that sparse nature of feature maps (FMs) due to the ReLU function lead to many existing attacks more obvious to the attacker. Next, we investigated how one can detect the existing attacks if the defender has some previous knowledge of the complete CNN’s FMs. Our results show minimal detection overhead of about 2%, with an accuracy of 95% and F1 score of above 0.97.1

Although recent performance improvements to Secure Multiparty Computation (SMPC) made it a practical solution for complex applications such as privacy-preserving machine learning (ML), other characteristics such as robustness are also critical for its practical viability. For instance, since ML training under SMPC may take longer times (e.g., hours or days in many cases), any interruption of the computation will require restarting the process, which results in more delays and waste of computing resources. While one can maintain exchanged SMPC messages in a separate database, their integrity and authenticity should be guaranteed to be able to re-use them later. Therefore, in this paper, we propose ReplayMPC, an efficient failure recovery mechanism for SMPC based on blockchain technology that enables resuming and re-synchronizing SMPC parties after any type of communication or system failures. Our approach allows SMPC parties to save computation state snapshots they use as restoration points during the recovery and then reproduce the last computation rounds by retrieving information from immutable messages stored on a blockchain. Our experiment results on Algorand blockchain show that recovery is much faster than starting the whole process from scratch, saving time, computation, and networking resources.

Cloud service providers offer robust infrastructure for rent to organizations of all kinds. High stakes applications, such as the ones in defense and healthcare, are turning to the public cloud for a cost-effective, geographically distributed, always available solution to their hosting needs. Many such users are unwilling or unable to delegate their data to this third-party infrastructure. In this demonstration, we introduce RESCU-SQL, a zero-trust platform for resilient and secure SQL querying outsourced to one or more cloud service providers. RESCU-SQL users can query their DBMS using cloud infrastructure alone without revealing their private records to anyone. It does so by executing the query over secure multiparty computation. We call this system zero trust because it can tolerate any number of malicious servers provided one of them remains honest. Our demo will offer an interactive dashboard with which attendees can observe the performance of RESCU-SQL deployed on several in-cloud nodes for the TPC-H benchmark. Attendees can select a computing party and inject messages from it to explore how quickly it detects and reacts to a malicious party. This is the first SQL system to support all-but-one maliciously secure querying over a semi-honest coordinator for efficiency.

With the increasing interest in Secure Multi-Party Computation protocols (MPC), there have been several works such as the SPDZ1protocol that tackled this problem under a malicious security with dishonest majority attack model. However, most of these MPC efforts assume that the nodes running the computations are also supplying the inputs, which is not a realistic assumption for many real-life applications. In this paper, we extend the SPDZ protocol to enable clients outsource data and computation to the clouds while ensuring the correctness of the results, in addition to integrity and confidentiality of the input and output. We guarantee that the computation among nodes is done correctly by verifying their output’s Message Authentication Codes (MACs) at the end. Specifically, we delegate this task to an honest server. Our approach strives to minimize the burden on clients while enabling cheating detection even when assuming a malicious attack model with dishonest majority.

Allocating several Virtual Machines (VMs) onto a single server helps to increase cloud computing resource utilization and to reduce its operating expense. However, multiplexing VMs with different security levels on a single server gives rise to major VM-to-VM cybersecurity interdependency risks. In this paper, we address the problem of the static VM allocation with cybersecurity loss awareness by modeling it as a two-player zero-sum game between an attacker and a provider. We first obtain optimal solutions by employing the mathematical programming approach. We then seek to find the optimal solutions by quickly identifying the equilibrium allocation strategies in our formulated zero-sum game. We mean by "equilibrium" that none of the provider nor the attacker has any incentive to deviate from one's chosen strategy. Specifically, we study the characteristics of the game model, based on which, to develop effective and efficient allocation algorithms. Simulation results show that our proposed cybersecurity-aware consolidation algorithms can significantly outperform the commonly used multi-dimensional bin packing approaches for large-scale cloud data centers.

The uncertainty due to performance variations of IC chips and resource sharing on multi-core platforms have significantly degraded the predictability of real-time systems. Traditional deterministic approaches based on the worst-case assumptions become extremely pessimistic and thus unpractical. In this article, we address the problem of scheduling a set of fixed-priority periodic real-time tasks on multi-core platforms in a probabilistic manner. Specifically, we consider task execution time as a probabilistic distribution and study how to schedule these tasks on multi-core platforms with guaranteed Quality of Service (QoS) requirements in terms of deadline-missing probabilities. Moreover, it is a well-known fact that the relationship among task periods, if exploited appropriately, can significantly improve the processor utilization. To this end, we present a novel approach to partition real-time tasks that can take both task execution time distributions and their period relationships into consideration. From our extensive experiment results, our proposed methods can greatly improve the schedulability of real-time tasks when compared with existing approaches.

Cloud data centers are widely employed to offer reliable cloud services. However, low resource utilization and high power consumption have been great challenges for cloud providers. Moreover, the rapid increase in demand for affordable cloud services magnifies the obstacles for proficient resource management policies. In this paper, we investigate how to improve resource utilization and power consumption in cloud data centers when delivering services with statistically guaranteed Quality of Service (QoS). We assume that the service provider hosts different types of services, each of which has request classes with different QoS requirements. Different from the traditional approaches that distribute workloads with different QoS levels on different Virtual Machines (VMs), we introduce an approach to pack requests of the same service type, even with different QoS requirements, into the same VM, and to remove potential failure requests in time to improve resource usage and energy cost. We formally prove that our algorithm can statistically guarantee QoS conditions in terms of deadline miss ratios. We develop a cloud prototype to empirically validate our proposed methods and algorithm. Our experimental results demonstrate that our approach can significantly outperform other traditional approaches in terms of QoS guarantees, power consumption, resource demand and electricity cost.

The increased resource sharing on multi-core platforms has posed significant challenges on the predictability of real-time systems. Cache memory partitioning has proven to be one of the most effective methods to improve the predictability and also the schedulability of real-time systems. In this paper, we study how to allocate cache memory of a multi-core platform when scheduling fixed-priority hard real-time tasks. As the bounded worst-case execution time (WCET) of a real-time task varies with its cache allocation, the challenges of this problem are twofold: how to judiciously allocate the cache memory among all real-time tasks and how to map real-time tasks to each core to improve the schedulability. To address these challenges, we develop an approach that takes into consideration not only the WCET variations with cache allocations but also the task period relationship and thus can significantly improve the schedulability of real-time tasks. Our simulation results, based on the SPEC CPU2000 benchmarks suite, show that our approach can increase the schedulability of real-time tasks up to four times when compared to other similar scheduling mechanisms.

Web applications grow tremendously in both scale and scope, the application patterns turn to be more and more sophisticated. It is important but challenging for service providers to lower the operational costs without degrading user experiences, especially in the case where a service provider's profit is closely related to the user experience (e.g. response time.) In this paper, we study the problem of efficiently scheduling multi-tier time sensitive applications on distributed computing platforms with respect to the user's Quality of Service (QoS) requirements. The efficiency refers to the QoS satisfaction with low average response times. The service provider must ensures that service requests be served successfully before end-to-end deadlines with certain probabilities. To solve this problem, we propose an approach to judiciously assign a deadline for each service tier. An application request is dropped if any one of its services misses its deadline. Our simulation results demonstrate that our approach can statistically guarantee the required QoS more efficiently than the other widely applied methods (e.g. acceptance control, first-come-first-serve, deterministic sub deadline assignment, etc.) irrespective of whether the resources are shared or not by multiple different applications.