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.

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.

With the rapid development of cloud computing and Internet of Things (IoT) technologies, large amounts of data collected from IoT devices are encrypted and outsourced to cloud servers for storage and sharing. However, traditional ciphertext retrieval schemes impose high computation and storage overhead on end users. Meanwhile, IoT devices with limited resources are difficult to adapt to large amounts of data computation and transmission, which leads to transmission delay and poor user experience. In this article, we propose a lightweight privacy-preserving ciphertext retrieval scheme based on edge computing (LPCR) by extending searchable encryption (SE) and ciphertext policy attribute-based encryption (CP-ABE) techniques. First, to avoid network delay and paralysis, we introduce edge servers into LPCR and design a collaboration mechanism between the user side and the edge servers. The user side only needs to accomplish lightweight computation and storage tasks, which greatly reduces their resource consumption. Second, we extend the basic ciphertext policy attribute-based keyword search (CP-ABKS) technique and design the Linear Secret Sharing Scheme (LSSS) access control algorithm with attribute values to hide access policies and attributes. In addition, to improve the retrieval accuracy, the document indexes and query trapdoors are set up by conjunctive keywords to help the cloud server locate exactly the data that the user wishes to query. Formal security analysis verifies that LPCR can achieve the security of chosen plaintext attack (CPA) and chosen keyword attack (CKA), and resist collusion attack. Simulation experiments prove that LPCR is lightweight and feasible.

With the rapid development of cloud computing, massive amounts of data are uploaded to cloud servers for storage. For privacy protection, sensitive data should be encrypted before outsourcing, and ciphertext retrieval technologies based on similarity come into being. In cloud computing with massive data, the efficiency and accuracy of retrieval are crucial. However, most of the current similarity retrieval schemes do not perform well in these two aspects. Therefore, we propose SESR scheme, a secure and efficient similarity retrieval scheme based on homomorphic encryption. Firstly, we use Hamming distance to calculate the similarity between the feature vector of the data and query vector from the data user. Secondly, the homomorphic encryption algorithm is used to encrypt data to protect data privacy. Furthermore, we creatively design a BK-KD tree structure that hierarchically implements similarity search and fine-grained access control, thereby speeding up the retrieval efficiency. In addition, we design a two-cloud-server cooperative retrieval model and a message authentication scheme, which ensure access pattern privacy security and the integrity of the transmitted data simultaneously. We also propose an improved SESR scheme. In this scheme, we use Simhash algorithm to generate feature vectors and query vectors, which reduces storage overhead. Finally, the security of SESR is formally proved and the simulation results show the efficiency and accuracy of the retrieval scheme.