As edge computing attains tremendous popularity, IoT devices always outsource their data to nearby edge servers for storing and pre-processing, which improves the efficiency of data processing and reduces the required network resources. For privacy-preserving, sensitive data is mostly encrypted before outsourcing. Nevertheless, large volumes of data in edge computing usually comes from multiple data sources, which means that they are encrypted with different secret keys, making it difficult for edge server to query and process. Existing solutions are mostly proposed for this problem in cloud computing, but they do not take into account that the limitations of computing and storage capabilities of edge devices will prevent them from performing computationally expensive operations. In this paper, we propose a lightweight privacy-preserving equality query scheme (LPEQ) in edge computing for the first time, which allows authorized users to perform equality query efficiently and privately on the encrypted data outsourced by multiple IoT devices. We also introduce a formal security model and prove that the LPEQ meets secure requirements against curious entities under this model. Meanwhile, our theoretical analyses and experimental evaluations demonstrate that the LPEQ performs better efficiency in terms of computation and communication while retaining privacy-preserving properties. Therefore, it is practical for applications in edge computing.

Location data have great value for facility location selection. Due to the privacy issues of both location data and user identities, a location service provider can not hand over the private location data to a business or a third party for analysis or reveal the location data for jointly running data analysis with a business. In this paper, we propose a newly constructed PSI filter that can help the two parties privately find the data corresponding to the items in the intersection without any computations and, subsequently, we give the PSI filter generation protocol. We utilize it to construct three types of aggregate protocols for facility location selection with confidentiality. Then we propose a ciphertext matrix compressing method, making one block of cipher contain lots of plaintext data while keeping the homomorphic property valid. This method can efficiently further reduce the computation/communication cost of the query process—the improved query protocol utilizing the ciphertext matrix compressing method is given followed. We show the correctness and privacy of the proposed query protocols. The theoretical analysis of computation/communication overhead shows that our proposed query protocols are efficient both in computation and communication and the experimental results of the efficiency tests show the practicality of the protocols.

With the advent of cloud computing, outsourced computing has emerged as an increasingly popular strategy to reduce the burden of local computation. Optimal location query (OLQ) is a computationally intensive task in the domain of big data outsourcing, which is designed to determine the optimal placement of a new facility from a set of candidate locations. However, location data are sensitive and cannot be shared with other enterprises, so privacy-preserving optimal location query becomes particularly important. Although some privacy-preserving works have been proposed, they still suffer from other challenges, such as irrevocable query permissions and high communication overhead. To overcome these challenges, we propose a revocable and privacy-preserving optimal location query scheme based on TEE (Trusted Execution Environment). We employ a basic hash structure within the TEE to compute the intersection data of both parties. We use the concept of reverse nearest neighbor (RNN) to assess the impact of candidates, and then select the optimal facility location. In addition, to implement the revocation of query permissions, we introduce a key refresh strategy that adopts identity and timestamp. We evaluate the performance of the proposed scheme using real datasets, and the experimental results indicate strong practicality.

With the rapid development of the Internet of Things (IoT), more and more user devices access the network and generate large amounts of genome data. These genome data possess significant medical value when researched. However, traditional genome analysis confronts security and efficiency challenges, including access pattern leakage, low efficiency, and single analysis methods. Thus, we propose a secure and efficient dynamic analysis scheme for genome data within a Software Guard Extension (SGX)-assisted server, called SEDASGX. Our approach involves designing a secure analysis framework based on SGXs and implementing various analysis methods within the enclave. The access pattern of genome data is always obfuscated during the analysis and update process, ensuring privacy and security. Furthermore, our scheme not only achieves higher analysis efficiency but also enables dynamic updating of genome data. Our results indicate that the SEDASGX analysis method is nearly 2.5 times more efficient than non-SGX methods, significantly enhancing the analysis speed of large-scale genome data.