Mobile cloud computing has become widely adopted for its convenience in data storage and sharing, but it also introduces challenges related to data privacy and security. To address these issues, public key authenticated encryption with keyword search (PAEKS) has emerged as a potential solution that ensures data privacy while resisting internal keyword guessing attacks (IKGAs). Unfortunately, most existing PAEKS schemes have limited adaptability to multi-user scenarios. Specifically, in PAEKS, ciphertext generation requires the participation of users' secret keys, which results in ciphertexts being unique, even when the same keywords are encrypted by different users. Con sequently, the number of trapdoors used to match the ciphertexts grows linearly with the amount of senders. Designing an efficient PAEKS scheme for multiple users remains an open challenge. In this paper, we propose CT-PAEKS, a lattice-based PAEKS scheme with constant trapdoor for data privacy-preserving in mobile cloud computing. CT-PAEKS introduces an additional administrator, enabling the receiver to generate a unified search trapdoor for ciphertexts from multiple senders. Additionally, it allows multiple senders to generate a single ciphertext for the same keyword encryption, thus avoiding ciphertext duplication. Furthermore, CT-PAEKS supports fast search during ciphertext matching, allowing all corresponding ciphertexts to be identified with a single match. We also formalize and prove the security of CT-PAEKS in the random oracle model. Comprehensive perfor mance evaluations indicate that our scheme outperforms prior arts, achieving the 1.7×-2.7× and 2.0×-4.4× reduction in terms of computational and communication overhead, respectively.

Network scanning is crucial for both network management and cybersecurity. However, due to the vast address space of IPv6, brute-force scanning is infeasible. Seed-based target generation algorithms have recently attracted considerable research attention. However, existing target generation algorithms lack a deeper exploration of patterns, leading to poor capture of dense regions and consequently low hitrate. To address this issue, we propose 6Global, a dynamic IPv6 active address scanning method assisted by global perspective. 6Global first performs rapid clustering of seed addresses based on their descriptive attributes. Then, for each cluster, patterns are generated in a bottom-up manner based on entropy, using subranges to represent patterns and resulting in denser patterns. Finally, dynamic scanning is conducted using these patterns. During scanning, the reward of each pattern is dynamically adjusted based on its active density and global statistics, which enhances the capability in capturing dense regions. Experimental results on six seed datasets show that 6Global overall outperforms seven baseline methods and demonstrates significant advantages across multiple datasets.

The inherent autocorrelation of time series data presents an ongoing challenge to multivariate time series prediction. Recently, a widely adopted approach has been the incorporation of frequency domain information to assist in longterm prediction tasks. Many researchers have independently observed the spectral bias phenomenon in neural networks, where models tend to fit low-frequency signals before high-frequency ones. However, these observations have often been attributed to the specific architectures designed by the researchers, rather than recognizing the phenomenon as a universal characteristic across models. To unify the understanding of the spectral bias phenomenon in long-term time series prediction, we conducted extensive empirical experiments to measure spectral bias in existing mainstream models. Our findings reveal that virtually all models exhibit this phenomenon. To mitigate the impact of spectral bias, we propose the FreLE (Frequency Loss Enhancement) algorithm, which enhances model generalization through both explicit and implicit frequency regularization. This is a plug-and-play model loss function unit. A large number of experiments have proven the superior performance of FreLE. Code is available at https://github.com/Chenxing-Xuan/FreLE.

Due to their simple security assessments, permutation-based pseudo-random functions (PRFs) have become widely used in cryptography. It has been shown that PRFs using a single n-bit permutation achieve n/2 bits of security, while those using two permutation calls provide 2n/3 bits of security in the classical setting. This paper studies the security of permutation-based PRFs within the Q1 model, where attackers are restricted to classical queries and offline quantum computations. We present improved quantum-time/classical-data tradeoffs compared with the previous attacks. Specifically, under the same assumptions/hardware as Grover’s exhaustive search attack, i.e. the offline Simon algorithm, we can recover keys in quantum time Õ(2n/3), with O(2n/3) classical queries and O(n2) qubits. Furthermore, we enhance previous superposition attacks by reducing the data complexity from exponential to polynomial, while maintaining the same time complexity. This implies that permutation-based PRFs become vulnerable when adversaries have access to quantum computing resources. It is pointed out that the above quantum attack can be applied to several cryptographic schemes, including PDMMAC and pEDM, as well as general instantiations like XopEM, EDMEM, EDMDEM, and others.

Introduced by Google in 2016, federated learning (FL) is a distributed machine learning framework to ensure data privacy amid the surge in big data. FL enables secure data sharing without accessing local data. Despite its advantages, it faces challenges due to the limited participation of the data owner. To address this, this article proposes the model value transfer incentive (MVTI) to enhance FL incentives for Artificial Intelligence of Things (AIoT). MVTI allows active participation of data requesters in FL training, addressing limited data owner engagement, and facilitating personalized model construction. The integrated model bail and contribution assessment mechanism ensures fair benefit redistribution. Using smart contracts (SCs) and interplanetary file system (IPFS) enhances security and reliability, ensuring transparent and tamper-resistant execution for secure transactions and data integrity. Our experiments highlight MVTI’s superiority in addressing FL incentive challenges for AIoT compared to state-of-the-art baselines on real-world datasets. We also demonstrate the compatibility of multiple gradient protections with incentive mechanisms, especially with gradient compression. The proposed SC-MVTI scheme is resilient and demonstrates the potential to significantly improve the overall efficacy of the FL system within incentive frameworks.

The emergence of heterogeneous data brings new challenges to Federated Learning (FL). Unlike homogeneous datasets, heterogeneous data are inherently characterized by sample domain center offsets, which makes traditional federated learning often suffer from poor model results due to catastrophic forgetting and increased communication costs when dealing with such data. To address this problem, we introduce FedNHN, a novel Personalized Federated Learning (PFL) algorithm. FedNHN regularizes the local training loss by quantifying the disparity between the local and global models to generate pseudo-data feature representations. Specifically, it generates two types of pseudo-data: Highly Pseudo and Perturbed Data, by employing the Fast Gradient Sign Method (FGSM) with both the global model and local data. Furthermore, FedNHN leverages the Hilbert-Schmidt Independence Criterion (DN-HSIC) to assess the distances between the feature representations of the generated data by the global model and the local model, respectively, and utilizes the Centered Kernel Alignment (CKA) similarity based on DN-HSIC to regulate the local training loss effectively. Our mathematical analyses show that our design can maintain the utility of the federated model under heterogeneous data, and extensive evaluations on three classical datasets illustrate the effectiveness and performance of our proposed implementation.

Air access networks have been recognized as a significant driver of various Internet of Things (IoT) services and applications. In particular, the aerial computing network infrastructure centered on the Internet of Drones has set off a new revolution in automatic image recognition. This emerging technology relies on sharing ground-truth-labeled data between unmanned aerial vehicle (UAV) swarms to train a high-quality automatic image recognition model. However, such an approach will bring data privacy and data availability challenges. To address these issues, we first present a semisupervised federated learning (SSFL) framework for privacy-preserving UAV image recognition. Specifically, we propose a model parameter mixing strategy to improve the naive combination of federated learning and semisupervised learning methods under two realistic scenarios (labels-at-client and labels-at-server), which is referred to as federated mixing (FedMix). Furthermore, there are significant differences in the number, features, and distribution of local data collected by UAVs using different camera modules in different environments, i.e., statistical heterogeneity. To alleviate the statistical heterogeneity problem, we propose an aggregation rule based on the frequency of the client’s participation in training, namely, the FedFreq aggregation rule, which can adjust the weight of the corresponding local model according to its frequency. Numerical results demonstrate that the performance of our proposed method is significantly better than those of the current baseline and is robust to different non-independent and identically distributed(IID) levels of client data.

Aiming at the problem that it is difficult to flexibly realize, the sharing and efficient search of encrypted data in large data-bases, this paper proposes a deterministic threshold proxy re-encryption scheme under the auxiliary input model. This scheme uses Shamir's secret sharing technology to achieve threshold control, uses homomorphic signature technology to verify the legitimacy of ciphertext, and applies deterministic algorithms to solve the search problem in large databases, while ensuring the user's control over their own data, and proves its security can reach indistinguishable semantic security (PRIV1-INDr) under the standard model. Compared with other schemes, this scheme not only shortens the length of the ciphertext and improves the decryption efficiency, but it also has anti-auxiliary input, robustness, and multi-hop characteristics and can better meet actual needs.

Blockchain technology has been widely used in digital currency, Internet of Things, and other important fields because of its decentralization, nontampering, and anonymity. The vigorous development of blockchain cannot be separated from the security guarantee. However, there are various security threats within the blockchain that have shown in the past to cause huge financial losses. This paper aims at studying the multi-level security threats existing in the Ethereum blockchain, and exploring the security protection schemes under multiple attack scenarios. There are ten attack scenarios studied in this paper, which are replay attack, short url attack, false top-up attack, transaction order dependence attack, integer overflow attack, re-entrancy attack, honeypot attack, airdrop hunting attack, writing of arbitrary storage address attack, and gas exhaustion denial of service attack. This paper also proposes protection schemes. Finally, these schemes are evaluated by experiments. Experimental results show that our approach is efficient and does not bring too much extra cost and that the time cost has doubled at most.

The emerging, overclocking signal‐based acoustic covert communication technique allows smart devices to communicate (without users’ consent) utilizing their microphones and speakers in ultrasonic side channels, which offers users imperceptible and convenient personalized services, e.g., cross‐device authentication and media tracking. However, microphones and speakers could be maliciously used and pose severe privacy threats to users. In this paper, we propose a novel high‐frequency filtering‐ (HFF‐) based protection model, named UltraFilter, which protects user privacy by enabling users to selectively filter out high‐frequency signals from the metadata received by the device. We also analyze the feasibility of using audio frequencies (i.e., ≤18 kHz) to the acoustic covert communication and carry out the acoustic covert communication system by introducing the auditory masking effect. Experiments show that UltraFilter can prevent users’ private information from leaking and reduce system load and that the audio frequencies can pose threats to user privacy.