With the continuous development of the Industrial Internet of Things (IIoT) and cloud computing, an increasing number of firms store industrial data in the cloud. However, data outsourcing raises owners’ concerns about the confidentiality of their data, prompting them to encrypt data before uploading. When all files are encrypted, it becomes tough to locate specific ones. Searchable encryption enables users to retrieve needed data from encrypted datasets. Most existing keyword-searchable encryption schemes are designed for single-user scenarios; yet in practice, multiple users often access the same data, making single-user schemes unsuitable for multi-user environments. Additionally, sharing data on untrusted devices may lead to single key leakage. This paper presents a new searchable encryption scheme that supports multi-user search. By integrating blockchain, it addresses the problem of single key leakage. The scheme is proven to be impervious to keyword-guessing attacks and achieves trapdoor unlinkability, further enhancing data confidentiality. Performance evaluations compared with existing schemes demonstrate the proposed scheme’s efficiency and feasibility in IIoT deployments.

In fog-based smart grids, smart meters collect users’ electricity data in real time and transmit it to fog node (FN) for aggregation. Then FN sends the aggregated data to control center for in-depth analysis. Throughout this process, data privacy preservation must be implemented, for which privacy-preserving data aggregation (PPDA) serves as a viable solution. However, most existing PPDA schemes do not support multidimensional data aggregation and fault-tolerance(i.e. scheme can still achieve data aggregation even when some devices malfunction), and these schemes are vulnerable to collusion attacks and have high computational and communicational overheads. To address these issues, this paper proposes a certificateless privacy-preserving multidimensional data aggregation scheme with fault-tolerance. The scheme first employs Chinese Remainder Theorem to integrate multidimensional data into a single data, followed by data obfuscation using the updated blind factors, and subsequently encrypts the data via the Paillier homomorphic encryption. Security analysis indicates that the scheme achieves data confidentiality and authenticity while resisting collusion attacks. Performance comparisons indicate its advantages in both communicational and computational overheads.

Location-based recommendation services (LBRS) offer great convenience to our daily lives, but ensuring that user privacy remains intact against the honest-but-curious service provider has become a key concern for the LBRS community. Current mainstream approaches to privacy protection in LBRS typically involve introducing a third party or trading off service accuracy. Considering the reality of limited mobile resources of LBRS users, current privacy-centric LBRS solutions tend to incur significant computation and communication costs. To address these shortcomings, we propose an efficient privacy-preserving scheme for LBRS that simultaneously achieves users' query content privacy, identity privacy, and the LBRS provider's data privacy. Specifically, we utilize linkable ring signcryption (LRSC) to achieve the anonymity of users' identities, confidentiality of the query content, and linkability of a user's multiple actions within a valid service period. The integration of an anonymous payment protocol into the LRSC scheme allows users to make anonymous payments and receive encrypted query results from the LBRS provider, guaranteeing privacy and security throughout the entire process. Under the random oracle model (ROM), our scheme is proven to be secure. Furthermore, it significantly outperforms prior works in efficiency, making it suitable for LBRS.

ABSTRACT Searchable Encryption (SE) enables searching over encrypted data. Exact keyword search is supported in most SE schemes, which achieve higher search accuracy but suffer from lower completeness due to the inability to handle similar expressions. To realize fuzzy keyword search, some schemes employ Bloom Filters (BFs), but these may incur high false positive rates and risk exposing the Bloom Filter's internal values to cloud servers (CS). Besides, most existing schemes ignore the fact that CS may engage in malicious behaviors (e.g., undercounting parameters or forging results). To address these issues, we propose an efficient and verifiable ranked fuzzy multi‐keyword search scheme based on BFs. We propose a Twin Bloom Filter (TBF) to conceal insertion positions and introduce random numbers to obfuscate uninserted bits. Search results are ranked using Term Frequency‐Inverse Document Frequency (TF‐IDF) scores to improve relevance. To ensure correctness and integrity, we employ Real Homomorphic Message Authentication Codes (RealHomMAC) and a random challenge technique, respectively. Security analysis proves that our scheme remains secure under both the known‐ciphertext model and the known‐background model. Theoretical and experimental performance analysis confirms that our scheme achieves efficient and accurate keyword search.

ABSTRACT Certificateless cryptography, which solves the key escrow problem and avoids the complexity of certificate management, is an important part of public key cryptography. In the multi‐user scenarios, broadcast encryption can improve computational efficiency and reduce communication cost. Moreover, there may be some malicious users in the above scenarios, and the decryption permissions of these users need an effective mechanism to revoke them. In this paper, a revocable certificateless encryption scheme for multi‐user (RCLE‐MU) is proposed to address this issue. In the scheme, the Cloud Server Provider (CSP) utilizes the master time key to periodically generate time keys for legitimate users. For illegitimate users, their decryption privileges are revoked since they are unable to obtain the time keys. Then this new scheme is proved to be ciphertext indistinguishable under selected identity and chosen‐ciphertext attacks (sID‐CCAs). Finally, compared with several other schemes, the new scheme has more efficiency advantage. So it is suitable for multi‐user scenarios.

In Cloud-Assisted IoT (CAIoT), the large amounts of data generated by devices and sensors need to be shared and stored efficiently and securely. Broadcast proxy re-encryption (BPRE) technology ensures data privacy and enables efficient sharing. A significant issue with existing BPRE schemes is that the re-encryption permissions of the cloud server are uncontrolled, potentially leading to re-encryption operations without the consent of the data owner, and increasing the risk of data leakage. Additionally, most conditional proxy re-encryption (CPRE) schemes rely on computationally intensive bilinear pairing operations, making them unsuitable for resource-constrained IoT devices. To address this, this paper proposes a new certificateless conditional broadcast proxy re-encryption scheme, where the data owner sets conversion conditions when generating the original ciphertext and re-encryption keys, ensuring that only ciphertext meeting the conditions can being converted, thus preventing the cloud platform from abusing re-encryption permissions. Security analysis shows that the proposed scheme can resist chosen ciphertext attacks and collusion attacks in the random oracle model. Performance evaluation shows that the proposed scheme avoids bilinear pairing and hash-to-point operations, reducing the computational cost for the data owner. This improves computational efficiency, making it more suitable for CAIoT scenarios.

With the rapid development of cloud computing technology, cloud services, represented by cloud storage and data retrieval, have been widely researched in Internet of Things (IoT). As a result, various data retrieval schemes have been proposed. The multikeyword Ranked Searchable Encryption Scheme (MRSE) was developed to improve the accuracy and experience of users searching data. However, MRSE has its drawbacks, such as the security risk of key leakage and limited functionality. Therefore, this article proposes a Privacy-preserving IoT Data Retrieval Scheme (PDRS) that supports lightweight fine-grained access control. We analyze the risk of key information leakage in MRSE, and perform permutation operations on encrypted indexes and trapdoors in PDRS to prevent key leakage and improve system security. Furthermore, in IoT scenarios with multiusers and multikeys, the secure user identity authentication mechanism ensures that only authorized users can acquire legitimate keys to generate search trapdoors, preventing malicious users from impersonating legitimate users and accessing private data. A novel polynomial-based access control is designed to realize attribute-based fine-grained access control, which enables resource-limited devices to limit data access to data users and achieves lightweight overhead. Finally, a formal theoretical analysis demonstrates that PDRS is secure. Simulation experiments verify that PDRS is efficient and lightweight.

Location-Based Services are increasingly common in our lives. In order to reduce user overhead, the data owner stores the location information and text data on the cloud server, and the user completes the retrieval task with the help of the fog server. To protect the privacy of outsourced data, many secure spatial keyword retrieval schemes have been proposed. Most schemes use R-tree indexes to improve the efficiency of ciphertext retrieval, but the encrypted R-tree index is hard to update. Moreover, some indexes based on order-preserving encryption are vulnerable to frequency-revealing attacks. So how to balance efficiency and security is a problem. To solve the above problems, we propose an efficient and secure spatial keyword ciphertext retrieval scheme based on cloud-fog collaboration. First, we innovatively design the SK-tree. The Geohash algorithm and Simhash algorithm are used in SK-tree to compress information, achieving efficient retrieval. Secondly, our retrieval tree has the function of fuzzy order preservation, which can better hide the correspondence between plaintext and ciphertext compared to traditional index-based order-preserving encryption schemes. In addition, we design a cloud-fog-user interaction scheme for attribute-based encryption that can hide access control policies, which reduces the computational overhead for the user side. Finally, we prove through theoretical analysis that our scheme ensures cloud data security and query trap information privacy. We compare our scheme with others through simulation experiments to demonstrate its superiority in efficiency.

In Internet of Things(IoT), the combination of cloud computing and edge computing becomes a new computing paradigm to provide users with low-latency data services. However, for the limited resource, high dynamic, and wide distributed characteristics of IoT devices, it becomes a great challenge to realize the universal application of edge servers and the cloud-edge computing allocation. Meanwhile, most of the schemes ignore the leakage of data access pattern privacy when accessing data. Therefore, in this paper, we propose a privacy-preserving access control scheme for IoT data. Based on the cloud-edge-end framework, we design a pervasive edge computing protocol, which allows well-resourced devices to become edge servers at suitable geographic locations and users to outsource and access IoT data through the nearest edge server. It increases the flexibility of the cloud-edge collaborative system as well as the efficiency of data processing. Users do not need to interact beyond the network edge to enjoy the data services. Furthermore, a novel attribute-based encryption scheme is designed based on a modified Lightweight Secret Sharing Scheme to optimize computing task allocation and reduce the computation burden on end devices, without attribute information leakage. In addition, we also design a Transform algorithm and a Cloud-edge Interaction protocol to hide access pattern privacy efficiently. We analyze the feasibility of the scheme and demonstrate that the scheme is semantically secure and conceals access pattern privacy. Simulation experiments based on real IoT data show that the scheme is efficient and suitable for IoT scenarios.

In big data scenarios, the data volume is enormous. Data computation and storage in distributed manner with more efficient algorithms is promising. However, most current ciphertext search schemes are designed for the centralized cloud computing platforms and they are inefficient and inapplicable in big data scenarios. A proxy server based system is a cloud computing extension. This new pattern moves some of the data storage and computation burden from end users to the edge servers and it greatly decrease the resource costs of data users. In this paper, we propose a searchable encryption scheme assisted by cloud computing and proxy servers for big data, which can accomplish Lightweight Fine-grained access control and Efficient multi-keyword top-k ciphertext Search synchronously (LFES). To cope with all types of data, we design an innovative fine-grained access control mechanism based on attribute-based encryption and key distribution protocol. Thus, the scheme only allows users with licensed attributes to access data efficiently. Then, a public key searchable encryption scheme is proposed based on privacy Protection Set Intersection (PSI) and the proxy server model. Our scheme greatly reduces the computation burden on end-users and improves retrieval efficiency. Meanwhile, to prevent tampering with stored ciphertexts, a practical data integrity audit mechanism is also designed. Security analysis illustrates that the LFES can resist Chosen Keyword Attack (CKA) and Keyword Guessing Attack (KGA). Finally, the simulation shows that the LFES is efficient and feasible in practice.

Abstract Cloud medical treatment provides real-time data sharing in a cost-effective method, making it more practical to create, collect and manage vast amounts of personal health records (PHR) of patients. However, health information is considered highly sensitive. How to securely store and dynamically process massive patients’ PHR data in a public cloud environment has become one of the most important challenges. Therefore, we introduce a novel solution to the problems of privacy exposure, data security and flexible access of storage modules in medical systems. In this paper, we present a privacy-preserving certificateless broadcast encryption with authorization for the PHR system, which is the best approach to effectively solve the above problems and avoid key escrow. In our work, users (patients) outsource their encrypted data to the cloud server and reallocate data accessing rights of recipients through an authorization set, sharing with a group of authorized receivers (doctors) in a secure and efficient manner. In addition, it is shown to be capable of achieving both plaintext confidentiality and receiver anonymity under the random oracle model. Moreover, the experimental evaluation shows that the proposed scheme enjoys low computational and communication overhead, indicating the feasibility and practicality of the scheme.

Abstract Federated learning enables training healthcare diagnostic models across multiple decentralized devices containing local private health data samples, without transferring data to a central server, providing privacy‐preserving services for healthcare professionals. However, for a model of a specific field, some medical data from non‐target participants may be included in model training, compromising model accuracy. Moreover, diagnostic queries for healthcare models stored in cloud servers may result in the leakage of the privacy of healthcare participants and the parameters of models. Furthermore, the records of model searching and usage could be tracked causing privacy disclosure risk. To address these issues, we propose a blockchain‐based privacy‐preserving searchable attribute‐based encryption scheme for the diagnostic model federated learning in the Internet of Medical Things (BSAEM‐FL). We first adopt fine‐grained model trainer participation policies for federated learning, using the attribute‐based encryption (ABE) mechanism, to realize model accuracy and local data privacy. Then, We employ searchable encryption technology for model training and usage to protect the security of models stored in the cloud server. Blockchain is utilized to implement distributed healthcare models' keyword‐based search and model users' attribute‐based authentication. Lastly, we transfer most of the computational overhead of user terminals in model searching and decryption to edge nodes, achieving lightweight computation of IoMT terminals. The security analysis proves the security of the proposed healthcare scheme. The performance evaluation indicates our scheme is of better feasibility, efficiency, and decentralization.

In the Internet of Vehicles (IoV), the data center will receive a large number of data-signature pairs sent by vehicle-mounted communication devices and needs to quickly verify the validity of the signatures. Using aggregated signatures, the validity of multiple signatures can be verified by performing only one verification calculation, which significantly improves the calculation efficiency and is very suitable for the Internet of Vehicles. Unfortunately, security proofs of most certificateless aggregate signature (CLAS) schemes are implemented in the random oracle model (ROM). It is known that schemes with provable security in ROM may be vulnerable in real situations. Therefore, there may be potential security risks when these schemes are applied to the IoVs. In this paper, the system model and security demands of a CLAS scheme for IoVs were put forward. Afterwards, a concrete scheme was constructed and the security proofs were achieved in the standard model (SM). In the end, the advantage enjoyed by new scheme was demonstrated by making a comparison on performance for several schemes.

In a data auditing scheme, the data owner authorizes a third-party auditor (TPA) to check whether the data stored in the cloud remains intact. Researchers have given many data auditing schemes. However, there are still three significant shortcomings in these schemes. First of all, the security proofs of these schemes are completed in the random oracle model (ROM). As we all know, a scheme with provably security in ROM may be insecure in practical applications. Secondly, TPA in most known schemes is set to be completely reliable. However, TPA in reality may attempt to extract the data owner's data. These schemes cannot resist the malicious behavior of TPA. Third, most known schemes require hash-to-point operations and enjoy high computation cost, so they are not suitable for computing constrained environments. In this paper, we first presented the system model and security demands for an identity-based data auditing (IBDA) scheme. We then came up with a new IBDA scheme and showed the security proofs in the standard model (SM).Finally, we made an analysis on performance for seven data auditing schemes. In our scheme, the computation cost required by TPA is a constant, independent of the number of data blocks participating in the challenge. Therefore, our scheme requires low computation cost and is suitable for computing-constrained environments

Abstract Identity‐based encryption (IBE) is a public key cryptosystem on purpose to remove the traditional certificate management. How to realize efficient user revocation in the IBE is of great importance when considering its application. The drawback of most existing works is that the revoked user can still access the ciphertext prior to revocation. One possible solution proposed by Sun et al. is to evolve the ciphertext in the cloud. However, it is unfortunate that the master time key in their scheme is kept by the private key generator (PKG), which means it is sustained extra burden. In this article, we present an efficient revocable IBE with ciphertext evolution in the cloud‐assisted system and the ciphertext remains constant size. The cloud server keeps only one secret master time update key sent by the PKG in a private channel, and thus our scheme offers scalability. In the meantime, our detailed analysis demonstrates that our proposed scheme is semantically secure against ciphertext chosen attacks based on the k‐CAA problem and enjoys better efficiency in computation and communication costs compared with previous works.

Safe and efficient collection of users’ electronic consumption data is a basic function of the smart grid. Anonymous aggregate signature is a fine option to realize this feature, which can not only reduce the computing cost of the data center (DC) but also protect the user’s identity information from leaking. The security proofs of all known identity-based aggregate signature (IBAS) schemes were completed in the random oracle model (ROM). Regrettably, these schemes are not necessarily secure in real life. In this article, we first propose the system model and security demands of an identity-based designated verifier anonymous aggregate signature (IB-DVAAS) scheme for smart grids. We then come up with a concrete scheme, only the designated DC can check the validity of an aggregate signature, but it cannot recognize the real identity of the signers. Next, we display the security proofs in the standard model. Finally, we conduct an analysis on performance for six aggregate signature schemes. Only three pairing operations are required and hash-to-point operations are not used, the calculation cost of the new scheme is lower than that of other schemes.

In healthcare wireless sensor networks (HWSNs), wireless sensors are placed on the patient, which collect the patient’s vital signs parameters and various environmental information, and send these data to the doctor, so that the doctor is able to perform real-time remote monitoring for the patient. The patient’s vital signs parameters contain a lot of private information. It is very likely to cause the leakage of patient’s private information if these parameters are transmitted through the public channel. In addition, many patients do not want doctors to know their true identity. We designed a certificateless anonymous signcryption (CL-ASC) scheme for HWSNs in this article. It encrypts the patient’s vital signs parameters, authenticates the legitimacy of the sensor, and realizes the anonymity of the sensor. We then showed the security proofs of new scheme in the standard model. Finally, we compared the performance of the six schemes. Since only three pairing operations are used, the new scheme enjoys higher computation efficiency and is suitable for HWSNs.

Provable data possession (PDP) solves the problem of determining whether the data stored in the cloud is maintained in its entirety. Certificateless cryptography simultaneously tackles two challenges: certificate management and key escrow. There are still three deficiencies in the current known certificateless provable data possession (CL-PDP) schemes. First, security proofs are typically conducted in the random oracle model (ROM). Second, some schemes are incapable of preventing third-party auditor (TPA) from recovering user data blocks. Third, most schemes require hash-to-point operations, which result in lower computational efficiency. In this article, we demonstrated two kinds of attacks targeting the scheme Ji et al. 2020. Subsequently, we put forward a new CL-PDP scheme and showed security proofs in the standard model (SM). Finally, we conducted performance analysis on ten CL-PDP schemes. Our scheme outperforms others in terms of efficiency, as it minimizes computational overhead by requiring only three pairing operations and eliminating the need for hash-to-point operations.

The contradiction between the growing demand for healthcare and the lack of professional medical staff urgently needs to be resolved. Healthcare wireless sensor technology is an important option to resolve this contradiction. Patients use wearable devices to collect various health indicators and send them to doctors for diagnosis in a timely manner. This breaks the time and space constraints in traditional medical methods. Patient's data is transmitted over a public network. Therefore, protecting data security and patient privacy has become an urgent issue for healthcare wireless sensor networks (HWSNs). In this article, we first presented a system model for certificateless designated verifier anonymous aggregate signature (CL-DVAAS) scheme for HWSNs, and investigated the security attributes. We then gave a concrete construction and showed the security proofs. We finally compared the new scheme with several other schemes, and the results show that the new scheme is more computationally efficient.