The integration of the Internet of Things (IoT) with cloud computing has expanded the scope of IoT applications but also introduced challenges in dynamic data management. Attribute-Based Encryption (ABE) serves as a crucial technology for constructing access control systems in data sharing environ ments. ABE schemes with policy updating capabilities allow data owners to dynamically and frequently modify access policies. Existing ABE schemes typically outsource the task of policy updating to a cloud server; however, the size of the update keys remains linear with the number of updated attributes and depends on the types of updated gates. The resulting updating costs are not less than generating a new ciphertext for data owners, especially when updating multiple attributes and threshold gates. Therefore, in this paper, we propose a novel ABE scheme, termed Policy Updatable Ciphertext-Policy ABE (PU CP-ABE), which enables efficient and flexible policy updating. Our construction ensures that the size of update keys depends only on the number of updating gates, independent of both the number of attributes and the gate types. Furthermore, PU-CP ABE provides a unified update mechanism that supports AND, OR, and threshold gates without requiring distinct update keys for different gate types.

Location sharing, as an essential component of mobile applications, helps mobile users share location information and enhance their community connections. However, users may be reluctant to share their locations with personal privacy concerns, as anyone including location server who knows these locations can infer much sensitive information about users through analyzing these locations’ information plus their background knowledge. Therefore, it poses a natural question for mobile location-sharing applications how to share users’ locations without any breach of their privacy. To answer this question, in this article, we describe two practical attribute-based encryption schemes served privacy-preserving mobile location-sharing applications. Our proposals are quite suitable for such a mobile application—location sharing with common interests since in our designs users’ interests are also taken into consideration as well as location information. In particular, our two schemes have a higher performance in the sense that in our first construction both ciphertexts and private keys are of constant size simultaneously, and our second construction is an extension of the first that provides a tradeoff between ciphertext size and public-key size. Therefore, the two schemes we designed in this work are quite practical in mobile applications which are often equipped with limited transmission or storage resources. Finally, we offer a formal security proof under a well-defined security model, followed by an experimental evaluation and a theoretical performance comparison.

As a common metric, maximum similarity between two objects is widely employed by web platforms to provide matching services. However, the calculation of maximum similarity involves numerous sensitive or confidential users’ data, and the web platform server is often not trusted who might peep these data out of curiosity, or even worse sell them to unauthorized entities to make profits. Therefore, many research lines on functional encryption have been suggested and studied on how to calculate the maximum similarity while ensure the privacy of users’ data. Unfortunately, all of them will divulge some intermediate results to the web platform server when processing this issue. In this paper we present a new functional encryption scheme supporting privacy-preserving maximum similarity, which enables the web service platforms to figure out the maximum similarity without learning anything else about their data. Moreover, we provide a formal analysis to prove the security of the proposed scheme, followed by some experimental evaluations and comprehensive comparisons with the related works. It shows that, our scheme is the first functional encryption realization on maximum similarity without divulging the intermediate result and meanwhile achieve a higher security-function privacy, as well as a traditional data privacy.

Cloud computing eliminates the need for local hardware, addressing the challenge of high computing expenses. However, entrusting data to the cloud may pose the risk of unintentional data loss. Using multiple copies and multi-cloud servers is promising because even if the data on one cloud storage server is compromised, the data proprietor can retrieve the information from alternate cloud storage servers. To protect data security, data needs to be encrypted before uploading to the cloud. However, users cannot directly confirm whether their encrypted documents and copies are stored securely and with integrity on cloud servers. To verify data copies on remote servers without downloading and decrypting, we propose Public Verification Public Key Encryption with Equality Test (PVPKEET). Under PVPKEET, users upload encrypted data to cloud servers, and then the test result and proof will be provided by the cloud server without decryption. The publicly verified proof can be examined by all users, allowing everyone to witness the copies stored correctly. Our approach is resistant to chosen-plaintext attacks and is verifiable. A comparison with prior research demonstrates the efficiency and feasibility of our design.

Privacy-preserving data evaluation is one of the prominent research topics in the Big Data era. In many data evaluation applications that involve sensitive information, such as the medical records of patients in a medical system, protecting data privacy during the data evaluation process has become an essential requirement. Aiming at solving this problem, numerous fuzzy encryption systems for different similarity metrics have been proposed in literature. Unfortunately, the existing fuzzy encryption systems either fail to achieve attribute-hiding or achieve it, but are impractical. In this article, we propose a new fuzzy encryption scheme for privacy-preserving data evaluation based on overlap distance, which can work in an integer domain while achieving attribute-hiding. In particular, we develop a novel approach to enable an accurate overlap distance to be fast calculated. This technique makes the number of pairing operations during decryption stage negative correlation with the size of the threshold, which is pretty practical for some applications especially with a large threshold. Additionally, we provide a formal security analysis of the proposed scheme, followed by a comprehensive experimental. Also we show that our scheme can be well applied to some scenarios, such as fuzzy keyword searchable encryption and attribute-hiding closest substring encryption.

Medicinal plants are the main source of natural metabolites with specialised pharmacological activities and have been widely examined by plant researchers. Numerous omics studies of medicinal plants have been performed to identify molecular markers of species and functional genes controlling key biological traits, as well as to understand biosynthetic pathways of bioactive metabolites and the regulatory mechanisms of environmental responses. Omics technologies have been widely applied to medicinal plants, including as taxonomics, transcriptomics, metabolomics, proteomics, genomics, pangenomics, epigenomics and mutagenomics. However, because of the complex biological regulation network, single omics usually fail to explain the specific biological phenomena. In recent years, reports of integrated multi-omics studies of medicinal plants have increased. Until now, there have few assessments of recent developments and upcoming trends in omics studies of medicinal plants. We highlight recent developments in omics research of medicinal plants, summarise the typical bioinformatics resources available for analysing omics datasets, and discuss related future directions and challenges. This information facilitates further studies of medicinal plants, refinement of current approaches and leads to new ideas.