With the rapid development of the internet-of-things (IoT), the application of IoT terminals (ITs) has been growing exponentially. To address this challenge, ultra-dense networks have been widely regarded as an effective solution. However, under the constraints of task latency and resource limitations, how to achieve the joint offloading and caching of energy efficiency and security remains a critical issue. To address it, we first propose two types of secure collaborative offloading modes for this network framework, i.e., secure collaborative computation offloading with caching and non-caching. Under these two modes, we then strive to minimize the overall local energy consumption (EC) of all ITs, subject to the constraints of computational resources, latency, security cost, and caching capacity. This is achieved by jointly optimizing device association, cache decision-making, channel selection, executing decision-making, power control, secure service allocation, and multi-step task offloading. To solve the formulated nonlinear fractional problem efficiently, we put forward an improved football team training algorithm (IFTTA), which integrates a diversity-guided mutation strategy into the original football team training algorithm (FTTA). Furthermore, we conduct an in-depth analysis of the convergence properties and computational complexity of the proposed algorithm. Simulation results demonstrate that the IFTTA achieves lower total local EC and task-processing delay compared to the FTTA, while satisfying system constraints, and generally outperforms existing state-of-the-art methods.

In industrial internet of things (IIoT) scenarios, the energy efficiency of task offloading is challenged by the quasi-periodic fading of wireless channels and the energy constraints of IIoT devices. To address this, we propose a multi-stage offloading framework, which allows intelligent reflecting surfaces (IRS)-assisted unmanned aerial vehicles (UAV) to dynamically reflect transmitted signals between a small base station (SBS) and a macro base station (MBS), aiming to mitigate inter-tier and cross-tier interference. However, achieving efficient offloading while minimizing energy consumption remains a critical challenge due to the complex interplay between device offloading decisions, IRS phase shift design, subchannel allocation, and power control. To tackle this, we first formulate a mixed-integer nonlinear programming problem based on uplink communication and computational models. Then, an improved escape optimization algorithm (IESC) is developed to solve the problem, which achieves efficient convergence through dynamic solution space exploration. Finally, simulation results demonstrate that our proposed scheme significantly outperforms existing benchmarks in terms of energy efficiency and offloading performance.

Neural network inference in cloud service offers tangible benefits to users, from individuals and small institutions to large companies. However, two crucial concerns must be addressed. The first arises in satisfying the privacy of the model, the input data, and the inference results throughout the inference process. The second pertains to verifying that the inferences are derived from the designated neural network model. Although Secure Multi-Party Computation (MPC) and Zero-Knowledge Proof (ZKP) are typically adopted to mitigate such issues, the major challenge lies in achieving privacy preservation and verifiability simultaneously. In this study, we address both issues by proposing VSecNN, a verifiable and privacy-preserving neural network inference scheme. Specifically, we integrate MPC with the Zero-Knowledge Succinct Non-Interactive Argument of Knowledge (zk-SNARK) protocol to achieve zero-knowledge proof generation for multiple parties. Subsequently, we perform adaptive optimizations on the multi-party proof generation approach to align with the neural network, thereby achieving both privacy-preserving capabilities and verifiability. Experimental results demonstrate an improvement in the efficiency. For example, the computation time for completing our multi-party proof generation could be as low as 1.7 times that of the single-party proof generation, while the verification requires only 169ms on the MNIST dataset.

In recent years, with the development of wireless communication networks, federated learning (FL) has been widely deployed in distributed scenarios as a privacy-preserving machine learning paradigm. Due to its inherent features, FL shows vulnerability to backdoor attacks. In a backdoor attack, an adversary manipulates the global model’s output by compromising the model of one or multiple participants. Existing backdoor attacks are constrained to outputting a single specified target label during the inference phase, limiting the adversary’s flexibility to alter the model’s output when different target labels are required. In this paper, we study the multi-target attack scenario within the federated learning context, where the adversary aims to manipulate the global model to output various specified labels by inserting different types of triggers. To effectively insert multiple backdoors simultaneously without reducing the attack’s effectiveness, we propose MulDoor, a novel multi-target backdoor attack scheme. MulDoor incorporates the concept of supervised contrastive learning to learn the discrepancies among different types of triggers and mitigate interference between them. The experimental results demonstrate that MulDoor achieves better attack effectiveness compared to existing backdoor attacks in a multi-target backdoor attack setting.

In this paper, we consider optimizing a multiple-input multiple-output (MIMO) dual-functional radar-communication (DFRC) transceiver at the roadside unit (RSU) to detect a potential eavesdropping target and transmit the private information securely to the legitimate vehicular users. An optimization problem is formulated by optimizing the sum secrecy throughput of vehicle-to-infrastructure (V2I) links under requirements of waveform similarity and target return signal-to-interference-plus-noise ratio (SINR) threshold. To handle the challenging issue, we first cast the resulting non-convex problem into an equivalent optimization problem relying on the mean-square error (MSE) technique specified resource budget, and then develop an alternating procedure to decouple two optimization variables and decompose the resulting problem as two subproblems. To deal with the subproblem, we propose a dual ascent approach (DAA) based on the Limited-memory Broyden Fletcher Goldfarb and Shanno (LBFGS). Simulation results confirm the efficiency of the devised optimization method.

Vehicle-to-vehicle (V2V) communication applications face significant challenges to security and privacy since all types of possible breaches are common in connected and autonomous vehicles (CAVs) networks. As an inheritance from conventional wireless services, illegal eavesdropping is one of the main threats to Vehicle-to-vehicle (V2V) communications. In our work, the anti-eavesdropping scheme in CAVs networks is developed through the use of cognitive risk control (CRC)-based vehicular joint radar-communication (JRC) system. In particular, the supplement of off-board measurements acquired using V2V links to the perceptual information has presented the potential to enhance the traffic target positioning precision. Then, transmission power control is performed utilizing reinforcement learning, the result of which is determined by a task switcher. Based on the threat evaluation, a multi-armed bandit (MAB) problem is designed to implement the secret key selection procedure when it is needed. Numerical experiments have presented that the developed approach has anticipated performance in terms of some risk assessment indicators.

With the emergence of more and more applications of Internet of Things (IoT) mobile devices (IMDs), a contradiction between mobile energy demand and limited battery capacity becomes increasingly prominent. In addition, in ultradense IoT networks, the ultradensely deployed small base stations (SBSs) will consume a large amount of energy. To reduce the network-wide energy consumption and prolong the standby time of IMDs and SBSs, under the proportional computation resource allocation and devices’ latency constraints, we jointly perform the device association, computation offloading, and resource allocation to minimize the network-wide energy consumption for ultradense multidevice and multitask IoT networks. To further balance the network loads and fully utilize the computation resources, we take account of multistep computation offloading. Considering that the finally formulated problem is in a nonlinear and mixed-integer form, we develop an improved hierarchical adaptive search (IHAS) algorithm to find its solution. Then, we give the convergence, computational complexity, and parallel implementation analyses for such an algorithm. By comparing with other algorithms, we can easily find that such an algorithm can greatly reduce the network-wide energy consumption under devices’ latency constraints.

The applications of social Internet of Things (SIoT) with large numbers of intelligent devices provide a novel way for social behaviors. Intelligent devices share images according to the groups of their specified owners. However, sharing images may cause privacy disclosure when the images are illegally distributed without owners’ permission. To tackle this issue, combining blind watermark with additive secret sharing technique, we propose a lightweight and privacy-preserving image sharing (LPIS) scheme with illegal distributor detection in SIoT. Specifically, the query user’s authentication information is embedded in two shares of the transformed encrypted image by using discrete cosine transform (DCT) and additive secret sharing technique. The robustness against attacks, such as JPEG attack and the least significant bit planes (LSBs) replacement attacks, are improved by modifying 1/8 of coefficients of the transformed image. Moreover, we adopt two edge servers to provide image storage and authentication information embedding services for reducing the operational burden of clients. As a result, the identity of the illegal distributor can be confirmed by the watermark extraction of the suspicious image. Finally, we conduct security analysis and ample experiments. The results show that LPIS is secure and robust to prevent illegal distributors from modifying images and manipulating the embedded information before unlawful sharing.

Summary Collaborative cloud applications have become the dominant application mode in the big data era. These applications usually generate plenty of cooperative files, which share their ownerships with all collaborative participants. Data deduplication is a promising solution to improve the storage efficiency and save the user expenditure. However, it remains an open issue on how to securely prove the shared ownerships for the shared files and address the attacks on account of using data deduplication. To tackle the above issue, in this paper, we introduce a novel concept of the Proof of Shared oWnership (PoSW) and construct a secure multi‐server‐aided PoSW (ms‐PoSW) scheme for securing client‐side deduplication for the shared files, which is based on the convergent encryption, secret sharing, and bloom filter. In the ms‐PoSW scheme, we employ a sharing convergent key to avoid the single point of failure, introduce the secret sharing algorithm to implement the shared ownership, and construct a novel interaction protocol between the shared owners and the cloud server to prove the shared ownership. Furthermore, a hybrid PoSW scheme is constructed to address the secure proof of hybrid cloud architectures. Finally, security analysis and performance evaluation show the security and efficiency of the proposed schemes.

The energy reduction for wireless systems becomes more and more important due to its impact on the operation cost and global carbon footprint. In this paper, we design two kinds of energy-efficient association schemes under an open loop power control for uplink heterogeneous cellular networks (HCNs), which are formulated as problems with maximizing sum energy efficiency (EE) and EE utility respectively. In them, the second scheme integrates with the load balancing level and user fairness. Since the first problem is in a simple form, we can easily solve it without any iteration. As for the second problem, we first introduce a dual variable to decouple the constraint and then develop a distributed algorithm using dual decomposition. In addition, we also give some convergence proofs for the proposed algorithms. In the simulation, we investigate the influences of different parameters on the association performance of designed association schemes.

Under the environment of the big data, the correlation between the data makes people have a greater demand for privacy. Moreover, the world has become more diversified and democratic than ever before. Freedom of speech is considered to be very important; thus, anonymity is also a very important security demand. The research of our paper proposes a scheme which can ensure both the privacy and the anonymity of a communication system, that is, the protection of message privacy while ensuring the users’ anonymity. It is based on anonymous identity‐based encryption (IBE), by which the users’ m e t a d a t a are protected. We implement our scheme in JAVA with Java pairing‐based cryptography library (JPBC); the experiment shows that our scheme has significant advantage in efficiency compared with other anonymous communication system. Internet‐of‐Things (IoT) involves many devices, and privacy of devices is very significant. Anonymous communication system provides a secure environment without leaking metadata, which has many application scenarios in IoT.

Topology control is an important approach to maintain the connectivity of cognitive radio networks (CRNs). Most existing works assumed that secondary users (SUs) must vacate the spectrum reclaimed by primary users (PUs), resulting the inefficient spectrum utilization. In this paper, we consider the simultaneous transmissions of SUs with PUs; meanwhile, SUs are equipped with successive interference cancellation (SIC) to mitigate the interference from PUs, thereby enabling SUs to access the spectrum more aggressively than previous works. Although SIC has been studied in the information theory and signal processing, it is not well investigated in guaranteeing the connectivity of wireless networks, especially the CRNs. On this account, we propose both centralized and distributed SIC-based topology control algorithm to alleviate the impact of the unpredictable activities of PUs and the potential interference between SUs. In particular, we integrate power control with channel assignment to construct a bi-channel-connected and conflict-free CRN with the fewest required channels. Theoretical analysis reveals that the bi-channel-connectivity and conflict-free properties can be ensured by our proposed algorithms. Then, simulation results demonstrate the effectiveness of proposed algorithms in terms of reducing the number of required channels and improving the robustness of topologies, as compared with the prevailing topology control algorithms.

Rapid development of cloud storage services produces a tremendous amount of user data outsourcing to cloud servers. Therefore, it is easy to generate data multi-replica, which is able to improve data availability and users' experience. However, when the management of data is poor, the sensitive information will be disclosed more easily. This may bring serious security and privacy challenges for both user's data and its multi-replica in cloud environment. In order to tackle the above issues, in this paper, we propose a multi-replica associated deleting scheme (MADS) in cloud environment. We first introduce a replica associated model to organize all of data replicas among different cloud servers. Furthermore, we propose the MADS scheme which is consists of data storage algorithm, replica generation algorithm, replica deletion and feedback algorithm. Finally, we employ Amazon S3 to implement MADS and the results indicate that the proposed scheme is available and effective.

In this paper, we study joint link scheduling and power control with successive interference cancellation (SIC), aiming at maximizing the α-utility. The joint link scheduling and power control with SIC (PCSIC) problem is formulated to be a mixed-integer non-linear programming (MINLP), which is NP-hard. In order to solve the problem, we first decompose the MINLP into three sub-problem and then propose an iterative algorithm. We compare our strategy with the scheme without power control from the perspective of system throughput, fairness index and energy consumption. Numerical results show the noticeable performance improvement of the proposed strategy.