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.

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.

Nowadays, cloud-edge communication has emerged as a promising communication paradigm, which leverages edge devices to provide a series of advantages, such as a fast response for end devices. However, considering complicated communication environments, a practical requirement is improving security by constructing decentralized and traceable communications. Currently, blockchains have been widely applied in cloud-edge communications to ensure decentralization and traceability by consensus. Despite these promising benefits, existing transparent and immutable blockchains inevitably introduce two limitations to data rights governance in blockchain-based cloud-edge communications. The first limitation is that transparent blockchains can hardly guarantee data confidentiality since data is accessible to all users, especially unauthorized users. The second limitation is that immutable blockchains can hardly support improper content redaction, which violates the right to be forgotten in GDPR. This paper proposes FDRG, the first fine-grained data rights governance scheme in blockchain-based cloud-edge communications. FDRG cryptographically ensures the right downward compatibility and user collusion resistance. Specifically, based on attributes and policies, FDRG partitions users into three roles (i.e., unauthorized user, readable user, and editable user) and ensures that editable users are compatible with the rights of readable users. The punchline is that FDRG leverages the linear secret sharing matrix-based secret sharing to govern the distribution of data decryption keys and chameleon hashes trapdoors. Formal security analysis proves the security of FDRG under the chosen-plaintext attack in the random oracle model. A full implementation on the FISCO blockchain platform shows that FDRG achieves competitive efficiency compared to state-of-the-art related schemes.