The Internet of Medical Things (IoMT) has rapidly developed due to its ability to enhance the efficiency of medical data collection and utilization. Encryption technology is vital for ensuring IoMT data security and privacy. However, existing solutions often fail when secret keys or random numbers are exposed under coercion, undermining their effectiveness and security. Additionally, medical data stored on cloud platforms is vulnerable to risks, such as tampering or loss. To address these challenges, we propose CR2-ABE, a novel encryption scheme specifically designed for the IoMT environment. CR2-ABE combines chameleon hash functions and deniable encryption techniques, enabling medical data owners and recipients to present deceptive messages under coercion, thereby enhancing the coercion resistance of sensitive medical data. Moreover, CR2-ABE employs ciphertext-policy attribute-based encryption (CP-ABE) to facilitate fine-grained access control for medical data, while also leveraging blockchain technology to ensure data integrity and tamper resistance within cloud services. In terms of user management, CR2-ABE implements a policy revocation mechanism that operates directly on ciphertexts using software Guard extensions (SGX). We rigorously prove the correctness and semantic security of CR2-ABE, demonstrating its resilience against coercion attacks. Comprehensive evaluation results show that CR2-ABE exhibits significant performance improvements in key generation, encryption, decryption, and policy revocation compared to other solutions. Therefore, CR2-ABE possesses strong security and scalability.

The rapid growth in the NFT (Non-fungible token) market has offered a wide variety of opportunities for scammers, fraudsters, wash tradings, and so on. One of the most urgent security issues is how to efficiently authorize and validate the ownership to make the NFT ecosystem avoid infringement and counterfeiting. By exploiting linear homomorphic tagging and robust digital watermarking technologies, this article proposes a generic framework for ownership authorization and batch validation of NFTs. Within this framework, the digital artwork creators can authorize the ownership to a buyer before the NFT is minted in the public blockchain. Anytime in the future who questions the ownership of a claimant can initiate a validation procedure to get an auditing report by running a Challenge-Response protocol that supports efficient batch verification. The completeness and soundness of the proposed framework have been proven by assuming a secure homomorphic tag scheme and a robust watermarking scheme. We also present instantiations of the generic construction, especially with$\Pi _{Pub}$, one can outsource the validation procedure to the public blockchain to release local computation burden. A series of elaborated experiments have shown our proposed framework is practical and efficient.

With the rapid development of smart cities, vehicles equipped with various sensors can effectively sense traffic, thus forming a crowdsensing paradigm for the intelligent transportation system (ITS). Although mobile crowdsensing in ITS has broad application advantages, it still faces many challenges, such as single point of failure, inefficient independent task allocation, and the inability to deal with safety emergency tasks in time. To handle the abovementioned issues, we establish a decentralized ITS architecture based on blockchain and propose the concurrent tasks assignment problem proved to be NP-hard and safety emergency tasks assignment problem. Then, we propose reinforcement learning-based concurrent tasks and the safety emergency tasks assignment method, which can maximize the utility of concurrent tasks based on satisfying the requirements of safety emergency tasks. Simulation results demonstrate the effectiveness of the proposed methods.

Crowdsourcing for healthcare, which is an application of crowd intelligence, has become a novel and important auxiliary way for traditional healthcare, showing a huge application perspective. In a crowdsourcing platform for healthcare, patients can act as requesters who recruit workers, such as doctors, to provide professional advice by posting a task. However, privacy concerns pose a significant obstacle for patients willing to participate in crowdsourcing, as task data often contain sensitive personal information. To address this issue, we propose a novel attribute-based, lightweight, and dynamic fine-grained worker selection scheme, called ABCrowdMed, with privacy-preserving features. With this scheme, requesters can select workers in a non-interactive way by using a novel CP-ABE scheme that incorporates online/offline encryption, verifiable outsourcing decryption, revocation, and hidden policy properties. Additionally, requesters can revoke and update their tasks by withdrawing some workers’ decryption privileges. Participants can also release the computation burden with the aid of a third-party server. The proposed scheme’s security has been proven to be selectively secure under the decisional$ (q-1)$assumption and satisfies forward/backward security. The performance of ABCrowdMed has been evaluated and compared with state-of-art schemes, with the results demonstrating that our scheme achieves the lowest computation and is suitable for resource-constrained settings.

Summary Due to the continuous development and progress of information technology, the Internet has also entered the era of big data based on the Internet of Things (IoT). How to protect the security of data stored and transmitted in the IoT is one of the urgent problems to be solved. This article focuses on the security issues of storage and transmission of image data in the IoT. Secret image sharing (SIS) is a kind of image protection mechanism by dividing an image into n shares, and different shares are given to different participants separately for preservation. Only when the number of shares reaches the threshold can the original image be recovered. From the perspective of image reconstruction mode, there are two types of SIS schemes: one is the traditional (k, n) threshold scheme, which provides an all‐or‐nothing reconstruction mode, the other is the progressive scheme, which can gradually restore the original image. In this article, a novel (k, k2) progressive secret image sharing based on modular operations is proposed, this method can divide the important images stored in the IoT into many parts and then transmit them to people in different places. It takes the whole as a unit in terms of the progressive recovery form. When the share reaches the threshold, certain blocks of the original image can be seen. As the share increases, the image will be clearer. When all shares participate in the reconstruction together, the original image can be restored without loss. Compared with other schemes, our scheme has the same smoothness, shadow size and satisfies the security, and is fine‐grained progressive.

Blockchain-enabled Internet of Things (IoT) provides a secure sharing of data and resources to the various miners of the IoT network, removes centralized control, and can overcome part of the existing challenges in traditional IoT. However, the IoT ecosystem faces some great challenges in terms of security, such as privacy leaking, eavesdropping, and so on, which seriously impede the deployment of the IoT ecosystem. Verifiable searchable encryption (SE) can make data owners (DOs) in the IoT dispel concerns about privacy and make data users (DUs) believe they get correct search results. For the blockchain-enabled IoT, this article proposes an efficient verification SE scheme with aggregation authorization and trusted revocation. With this scheme, DOs are willing to share their data to DUs for reward in a secure, efficient, and trusted way. For the DU, the DO can generate an aggregating key of a subset of encrypted documents, and then the DU has the privilege to search these documents and to verify the search results. By utilizing the trusted execution environment, the DO can revoke the search privilege of the DU. We present the analysis to show our proposed scheme achieves confidentiality, soundness, and fair payment at the same time. With the performance evaluations, we prove our proposal practical for blockchain-enabled IoT in terms of computational and smart contracts overhead.

Blockchain‐based crowdsourcing systems can mitigate some known limitations of the centralized crowdsourcing platform, such as single point of failure and Sybil attacks. However, blockchain‐based crowdsourcing systems still endure the issues of privacy and security. Participants’ sensitive information (e.g., identity, address, and expertise) have the risk of privacy disclosure. Sensitive crowdsourcing tasks such as location‐based data collection and labeling images including faces also need privacy‐preserving. Moreover, current work fails to balance the anonymity and public auditing of workers. In this paper, we present a secure blockchain‐based crowdsourcing framework with fine‐grained worker selection, named PrivCrowd which exploits a functional encryption scheme to protect the data privacy of tasks and to select workers by matching the attributes. In PrivCrowd, requesters and workers can achieve both exchange and evaluation fairness by calling smart contracts. Solutions collection also can be done in a secure, sound, and noninteractive way. Experiment results show the feasibility, usability, and efficiency of PrivCrowd.

Abstract Recently, much attention has been focused on designing provably secure cryptographic primitives in the presence of key leakage, even the continuous leakage attacks. However, several constructions on the (continuous) leakage-resilient certificate-based encryption (CBE) scheme were proposed based on the bilinear pairings, and the corresponding computational efficiency is lower. Also, the leakage on the master secret key is omitted in the previous constructions. In this paper, to further achieve the better performance, a new construction method of continuous leakage-resilient CBE scheme without bilinear pairings is proposed, and the chosen-ciphertext attacks security of designed scheme is proved based on the hardness of the classic decisional Diffie–Hellman assumption. The performance analysis shows that our method not only can obtain higher computational efficiency but also enjoys better security performances, such as the leakage parameter of secret key of user has the constant size, and an adversary cannot obtain any leakage on the secret key of user from the corresponding given ciphertext etc. The advantage is that our proposal allows leakage attacks of multiple keys, i.e. continuous leakage resilience of the secret key of user and bounded leakage resilience of the master secret key. Additionally, to provide the leakage resilience for the cloud computing, a novel data access control scheme for cloud storage service is proposed from our continuous leakage-resilient CBE scheme, which can keep its claimed security in the leakage seting.

Abstract In the actual applications, an adversary can break the security of cryptography scheme through various leakage attacks (e.g. side-channel attacks, cold-boot attacks, etc.), even the continuous leakage attacks. That is, a practical cryptography scheme must maintain its claimed security in the continuous leakage setting. However, the previous constructions on the leakage-resilient identity-based encryption (IBE) scheme could tolerate a leakage that is bounded, and cannot resist the continuous leakage attacks. In order to further achieve the better security, a novel method to build the continuous leakage-resilient IBE scheme with tight security is presented in this paper, and the scheme’s security is proved, in the standard model, based on a stronger security assumption that depends on the number of queries made by the adversary. In addition, our proposal has several advantages over previous such constructions, e.g. shorter public parameters, higher communication efficiency, tight security, etc.

Leakage of private information has become a threat to the security of computing systems. It has become a common security requirement that a cryptography scheme should withstand various leakage attacks, even the continuous leakage attacks. However, in the current constructions on the (continuous) leakage‐resilient identity‐based encryption (CLR‐IBE) scheme, the leakage parameter is a fixed value. Aiming to solve these problems, in this study, the authors show how to construct the CLR‐IBE scheme, and the adaptive chosen‐ciphertext attacks security of proposed construction can be proved in the standard model. To further improve the practicability of CLR‐IBE scheme, they design an improved IBE scheme with continuous leakage amplified property, and the leakage parameter has an arbitrary length.

Massive data are generated and collected by devices in the industrial Internet of Things. Data sources would encrypt the data and send them to the data center through the gateway. For some supervision purpose, the gateway needs to observe the encrypted data stream and label the suspicious data. Instead of decrypting ciphertext at the gateway, which is not efficient, this paper presents a Φ -searchable functional encryption scheme that supports inner product evaluations on encrypted data. Based on this scheme, an approach enabling various queries on the encrypted industrial data stream is proposed. The adaptive security of our proposed underlying functional encryption scheme can be proven under general subgroup decision assumptions, and our scheme has the smaller public key, the smaller secret key, and the smaller ciphertext size compared to the related schemes. In addition, the experimental results show that our proposed scheme is efficient. Especially for the gateway, querying on the encrypted data only needs less than 20ms, which is practical for industrial data stream auditing scenario.