To boost the encoder stealing attack under the perturbation-based defense that hinders the attack performance, we propose a boosting encoder stealing attack with perturbation recovery named BESA. It aims to overcome perturbation-based defenses. The core of BESA consists of two modules: perturbation detection and perturbation recovery, which can be combined with canonical encoder stealing attacks. The perturbation detection module utilizes the feature vectors obtained from the target encoder to infer the defense mechanism employed by the service provider. Once the defense mechanism is detected, the perturbation recovery module leverages the well-designed generative model to restore a clean feature vector from the perturbed one. Through extensive evaluations based on various datasets, we demonstrate that BESA significantly enhances the surrogate encoder accuracy of existing encoder stealing attacks by up to 24.63% when facing state-of-the-art defenses and combinations of multiple defenses.

The development of 6G mobile networks will produce many smart Internet of Things devices and data at the network’s edge. Advanced AI technology holds the potential to enable 6G mobile networks to collect and analyze this data for innovative applications and intelligent services. However, the inherent privacy constraints and limited communication resources within 6G mobile networks often make direct data transmission to servers undesirable. Federated learning (FL) is seen as a promising approach to address these problems. Yet, integrating FL into 6G mobile networks presents data heterogeneity issues. In this article, we design and propose a privacy-preserving FL scheme in 6G mobile networks. The core idea of this scheme is to take both dataset size and the discrepancy between local and global category distributions into consideration to compute the weights of different clients and apply threshold Paillier cryptosystem to perform weighted aggregation on client-encrypted data. Security analysis and experimental results demonstrate the advantages of this scheme in guaranteeing privacy preservation and improving training accuracy. Finally, we present some future directions for the integration of FL and 6G mobile networks.

With integrity and traceability, blockchains have been widely applied in Internet of Things (IoT) systems. However, immutable blockchains contradict recent data regulations (e.g., the right to be forgotten in General Data Protection Regulation), making redactable blockchain-based IoT emerge as a promising paradigm. In this paradigm, IoT users can specify expressive policies (i.e., containing multiple logical AND and OR operators) to achieve controllable data editability. Unfortunately, existing related schemes with expressive policies face several issues: high communication costs, data privacy leakage (i.e., data can be read by all users), and inefficient user revocation. This article proposes a privacy-preserving and revocable redactable blockchain scheme in IoT systems, named BlockENC. BlockENC allows owners to specify expressive policies for controlling which users can read or edit their data and ensures downward compatible privileges (i.e., editable users own the privilege of readable users but not vice versa) under only $\mathcal {O}(n)$ communication costs $(\mathcal {O}(n^{2})$ in other schemes). The punchline of BlockENC is to define readability policies as subsets of editability policies and introduce access control trees to embed these policies in distributing data decryption keys and chameleon hash trapdoors. Moreover, drawing inspiration from ciphertext division mechanisms in proxy re-encryption techniques, BlockENC creates globally unique random values to reconstruct user keys, converting updating all existing keys or ciphertexts when user revocation cases occur into simply invalidating corresponding keys. Security analysis proves that BlockENC is secure against chosen-plaintext attacks. Experiments on the FISCO blockchain platform show that BlockENC achieves around $5\times $ computation and $10\times $ communication improvement over related works.

In low-altitude economy (LAE) networks, artificial general intelligence (AGI) models play a critical role in tasks, such as path planning, object recognition, and task allocation. Support vector machine (SVM) models serve as fundamental components in AGI frameworks due to their robust capabilities in classification and regression tasks, which are essential for decision-making in LAE networks. However, distributed deployment and real-time inference of SVM models face significant challenges in security and privacy protection, including leakage of model parameters, exposure to data privacy, and reliability of prediction results. To address these issues, we propose a privacy-preserving and verifiable SVM prediction scheme (pvSVM) that can achieve the desirable properties of model privacy, data privacy, and private/public prediction verifiability. To be specific, we employ homomorphic encryption in conjunction with secret sharing to realize efficient and privacy-preserving model prediction in the edge. Then, we design two secure verification strategies to allow UAVs and any third party to check the correctness of predictions. To further support the verification of large-scale predictions, our scheme uses batch verification to reduce computational and communication overheads. Detailed analysis and extensive experiments prove the security and efficiency of our scheme.