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With the rapid development of emerging network technologies such as cloud computing, the background server-side of public health information services is widely deployed on the untrusted cloud, which has become one of the main threats of user health privacy leakage. To this end, this paper proposes an agent-based algorithm for the protection for user privacy health topics based on identity replacement. Its basic idea is to deploy a group of intermediate agents between the server-side and the client-side, to replace the identity of each health service request issued by client users and then submit it to the server-side, thereby, making it difficult to identify the real user corresponding to each request, and then improving the security of user health topic privacy on the completely-untrusted server-sides. Then, this paper proposes a client-based algorithm for the selection of intermediate agents, which evenly distributes the request data issued by client users to all the agents after topic identification and privacy computation for the request data, to improve the security of user health topic privacy on the incompletely-trusted agent-side. Finally, both theoretical analysis and experimental evaluation demonstrate the effectiveness of the proposed method, i.e., it can effectively improve the security of user privacy health topics on the untrusted server-side, under the premises of no changes to user usage habits, server-side architecture, service algorithm, service accuracy and service efficiency, so as to provide a theoretical and technical foundation for building a privacy-preserving platform for public health information services. [ABSTRACT FROM AUTHOR]

The arrival of cloud computing age makes data outsourcing an important and convenient application. More and more individuals and organizations outsource large amounts of graph data to the cloud computing platform (CCP) for the sake of saving cost. As the server on CCP is not completely honest and trustworthy, the outsourcing graph data are usually encrypted before they are sent to CCP. The optimal route finding on graph data is a popular operation which is frequently used in many fields. The optimal route finding with support for semantic search has stronger query capabilities, and a consumer can use similar words of graph vertices as query terms to implement optimal route finding. Due to encrypting the outsourcing graph data before they are sent to CCP, it is not easy for data customers to manipulate and further use the encrypted graph data. In this paper, we present a solution to execute privacy-guarding optimal route finding with support for semantic search on the encrypted graph in the cloud computing scenario (PORF). We designed a scheme by building secure query index to implement optimal route finding with support for semantic search based on searchable encryption idea and stemmer mechanism. We give formal security analysis for our scheme. We also analyze the efficiency of our scheme through the experimental evaluation. [ABSTRACT FROM AUTHOR]

Due to the advantages of pay-on-demand, expand-on-demand and high availability, cloud databases (CloudDB) have been widely used in information systems. However, since a CloudDB is distributed on an untrusted cloud side, it is an important problem how to effectively protect massive private information in the CloudDB. Although traditional security strategies (such as identity authentication and access control) can prevent illegal users from accessing unauthorized data, they cannot prevent internal users at the cloud side from accessing and exposing personal privacy information. In this paper, we propose a client-based approach to protect personal privacy in a CloudDB. In the approach, privacy data before being stored into the cloud side, would be encrypted using a traditional encryption algorithm, so as to ensure the security of privacy data. To execute various kinds of query operations over the encrypted data efficiently, the encrypted data would be also augmented with additional feature index, so that as much of each query operation as possible can be processed on the cloud side without the need to decrypt the data. To this end, we explore how the feature index of privacy data is constructed, and how a query operation over privacy data is transformed into a new query operation over the index data so that it can be executed on the cloud side correctly. The effectiveness of the approach is demonstrated by theoretical analysis and experimental evaluation. The results show that the approach has good performance in terms of security, usability and efficiency, thus effective to protect personal privacy in the CloudDB. [ABSTRACT FROM AUTHOR]

Publish/subscribe has been successfully used in a variety of information dissemination applications. However, in a cloud computing environment, the enormous amount of information results in a very high requirement for the computing performance of a publish/subscribe method. In this paper, we propose an efficient index called Enindex for publish/subscribe matching. First, we group all the subscriptions submitted by subscribers, based on the key attributes (i.e., the most frequent attributes occurring in the subscriptions). Second, we group all the predicates contained in the subscriptions, according to three basic operators: ≥ (greater),=(equal), and ≤ (less), so as to remove the repeated predicates, and thus reduce the memory overhead. Finally, we propose an effective index structure to combine the grouped subscriptions together with the grouped predicates. Enindex not only has a small memory overhead, but also can support efficient publish/subscribe matching and online subscription updating. We conduct extensive experiments on synthetic datasets, and the experimental results demonstrate the superiority of the Enindex over state-of-the-art methods in terms of memory overhead and computing efficiency. [ABSTRACT FROM AUTHOR]

Outsourcing data to the cloud offers various advantages, such as improved reliability, enhanced flexibility, accelerated deployment, and so on. However, data security concerns arise due to potential threats such as malicious attacks and internal misuse of privileges, resulting in data leakage. Data encryption is a recognized solution to address these issues and ensure data confidentiality even in the event of a breach. However, encrypted data presents challenges for common operations like access control and range queries. To address these challenges, this paper proposes Secure Multi-dimensional Data Retrieval with Access Control and Range Search in the Cloud (SMDR). In this paper, we propose SMDR policy, which supports both access control and range queries. The design of the SMDR policy cleverly utilizes the minimum and maximum points of buckets, enabling the SMDR policy is highly appropriate for supporting range queries on multi-dimensional data. Additionally, we have made modifications to Ciphertext Policy-Attribute Based Encryption (CP-ABE) to enable effective integration with the SMDR policy, and then constructed a secure index using the SMDR policy and CP-ABE. By utilizing the secure index, access control and range queries can be effectively supported over the encrypted multi-dimensional data. To evaluate the efficiency of SMDR, extensive experiments have been conducted. The experimental results demonstrate the effectiveness and suitability of SMDR in handling encrypted multi-dimensional data. Additionally, we provide a detailed security analysis of SMDR. [ABSTRACT FROM AUTHOR]