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.

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.

Internet of Medical Things (IoMT) has garnered significant research attention from both academic and medical institutions. However, the sensitive medical data involved in IoMT raises security and privacy concerns. To mitigate these, ring signature has surfaced as a proper solution, which offers unforgeability and anonymity. Unfortunately, most multi-signer ring signature schemes require a predetermined number of signers and are difficult to adjust dynamically. Additionally, traditional ring signatures have limited adaptability for IoMT due to their reliance on a single entity. It is challenging to effectively define different signature permissions for users of various entities, such as patients and doctors. Therefore, research focusing on constructing a dynamic multi-signer ring signature for multi-party participation remains a critical and ongoing challenge. In this paper, we present ASMR, an anonymity-enhanced sequential multi-signer ring signature scheme for secure medical data sharing in IoMT. ASMR contains two different rings, PR and DR, for patients and doctors, respectively. It allows patients in PR to anonymously sign their electronic healthcare record (EHR) owned by doctors in DR, overcoming the reliance on a single entity in existing approaches while enhancing the anonymity of the signature. Meanwhile, ASMR introduces the concept of signature chaining, allowing multiple users in DR to co-sign information in sequence. In addition, it ensures that each generated signature is traceable, offering a transparent system. We also formally prove the security of ASMR in the random oracle model. Comprehensive performance evaluations indicate that ASMR excels in both computational and storage overhead. In the best case, computational overhead is reduced by approximately 4.7×-61.7×, while storage overhead is reduced by approximately 26.7×-212.8× compared to prior arts.

The Industrial Internet of Things (IIoT) has been widely studied, which dramatically enhanced the manufacturing efficiency and service elasticity. However, how to ensure the data confidentiality and security in the event-driven publish/subscribe communication model becomes a cumbersome problem. To address this concern, ring signatures have been researched deeply. Nevertheless, existing solutions have large computational burdens and neglect to incorporate reporting and tracing features, which makes it impractical for IIoT. In this way, research focus on designing an efficient report and traceable ring signature is still far-reaching. In this article, we propose RAT ring, a novel report and traceable ring signature, which provides publisher authentication, anonymous communication, reporting, and tracing. To achieve this, we adopt the zero knowledge proof to verify the authenticity of publisher data, and the signature of knowledge to trace the signature. Then, we formalize and prove the security of our scheme. Eventually, through comprehensive performance evaluation, our scheme outperforms prior works by approximately up to 51 times in terms of total computational overhead. These results demonstrate that our design is practical and effective for data privacy-preserving in IIoT.

With the widespread development of database systems, data security has become crucial when it comes to sharing among users and servers. A straightforward approach involves using searchable encryption to ensure the confidentiality of shared data. However, in certain scenarios, varying user tiers are granted disparate data searching privileges, and administrators need to restrict the searchability of ciphertexts to select users exclusively. To address this issue, public key encryption with authorized keyword search (PEAKS) was proposed, wherein solely authorized users possess the ability to conduct targeted keyword searches. Nonetheless, it is vulnerable to resist quantum computing attacks. As a result, research focusing on authorizing users to search for keywords while achieving quantum security is far-reaching. In this paper, we propose a lattice-based variant of PEAKS (L-PEAKS) that enables keyword dataset authorization for outsourced data management. Unlike existing schemes, our design incorporates identity-based encryption (IBE) to overcome the bottleneck of public key management. Besides, we utilize several lattice sampling algorithms to defend against attacks from quantum adversaries. Specifically, each authorized user must obtain a search privilege from an authority. The authority distributes an authorized token to the user within a specific time period, and the user generates a trapdoor for any authorized keywords. Our scheme is proven to be secure against IND-sID-CKA and T-EUF security in a quantum setting. We also conduct comprehensive evaluations on a commodity machine to assess completeness and provide theoretical complexity comparisons with existing state-of-the-art schemes.

With emerging technology, the blockchain-based Vehicle Ad-hoc Network (VANET) can alleviate traffic congestion and optimize resource management to improve the transportation system's efficiency significantly. However, since the information transmitted in VANET is distributed in an open environment, security, and privacy are now critical issues. Recently, many authentication protocols based on Tamper-Proof Devices (TPD) have been proposed to solve the above-mentioned hindrances, and most of them rely on the ideal TPD with extreme security assumptions. Moreover, when the aggregate signature verification fails, these schemes can only completely discard the aggregate signature, which is impractical for VANET. In order to solve the above problems, we propose a more realistic TPD-based identity verification scheme with a privacy protection function for the blockchain-based VANET. Specifically, it uses an offline self-update method to periodically update the data in the TPD to resist side-channel attacks. Our performance analysis shows that the proposed scheme is superior to the existing schemes regarding security and average delay, so it is more suitable for the actual blockchain-based VANET environment.