Decentralized federated learning (DFL) has been widely used in edge computing and Internet of Things (IoT) settings with many devices. However, the heterogeneity of devices (e.g., varying computational capacity, stability, security requirements) can impact the performance of DFL applications. Our proposed SSAA, a secure aggregation scheme for DFL on heterogeneous devices, presented in this paper is designed to improve the efficiency of DFL while preserving privacy. Specifically, SSAA accelerates aggregation by synchronously coupling aggregation device-set formation with aggregation computation, and can cope with device availability and performance variability to maintain stable and efficient aggregation. By extending homomorphic encryption to support cross-round ciphertext continuity, SSAA enables reliable and secure decryption under large-scale dropouts in the original aggregation set, making it practical for dynamic DFL environments. In addition, we prove that SSAA is semi-honestly secure and resistant to device collusion attacks – fundamental security requirements for applications involving heterogeneous devices. We also implement SSAA and comprehensively evaluate its performance to demonstrate its practicability.

Dynamic searchable symmetric encryption (DSSE) allows efficient searches over encrypted databases and also supports clients in their updating of the data, such as those stored in a remote cloud server. However, recent attacks suggest the risk of leakage during such updates, which consequently impacts on the privacy of the queries. In addition, existing DSSE schemes that support forward privacy generally rely on the honest-but-curious server and support only single-keyword retrieval, which limits the application scenarios. In this paper, we present the design of a verifiable DSSE protocol, which supports efficient conjunctive query with forward privacy. In our scheme, the forward index is constructed by a novel form, i.e.,$ t$-puncturable PRFs, and the authentication tag is designed by symmetric cryptography. During conjunctive queries, we narrow the scope by an inverted index, and then we determine the results of the final query through the forward index. Meanwhile, we can use verification tag to check the correctness and completeness of the result. In addition, we present an extension to support backward privacy, and our experimental evaluations show that our proposed approach achieves better performance on both conjunctive queries and updates than other competing solutions and ensures efficient verification.

Federated learning (FL) has been used to enhance privacy protection in edge computing systems. However, attacks on uploaded model gradients may lead to private data leakage, and edge devices frequently joining and leaving will impact the system running. In this paper, we propose a dynamic and flexible federated edge learning (FEL) scheme that can defend against malicious edge servers and edge devices to recover sensitive data and efficiently manage edge devices. A heterogeneity-aware scheduling strategy is designed to take into account the different impacts of heterogeneous edge devices on global model performance. The strategy determines the order of devices participation in each round based on the relative contribution level of the online edge device model, and the edge device with the highest contribution level is selected first. Numerical experiments show that our system improves test accuracy and time, and the security analyses show that our scheme meets the security requirements.

Electronic wearable devices play an important role in the Internet of Things (IoT) systems for collecting medical data. Searching over such numerical medical data help to provide better service and treatment. However, user and data security is a major barrier to public adoption. Existing approaches designed to facilitate secure (range) searches over encrypted data generally incur expensive computational overhead suffer from unexpected information leakage, and/or have high false-positive results. Therefore, in this article, we design a hybrid searchable encryption scheme that supports efficient, secure, and accurate range searches over encrypted data sensed and collected from medical IoT devices. The designed graph structure helps to filter out most of the false data, and the batching processing on ciphertexts accelerates the removal of irrelevant data. Unlike most prior works, the proposed random index hides the distribution of data, and the probabilistic fixed-length trapdoor hides the range size and repetition of the query. If necessary, all the encrypted data can be refreshed by the cloud server after a range search. The scheme is proven to be secure in a simulation-based model. Then, we evaluate the performance of our proposed scheme on Microsoft Azure cloud servers and Azure IoT Central. The comparisons with several prior works demonstrate that our scheme supports more efficient secure range searches.

The outsourcing of data is becoming increasingly commonplace as data is constantly been synchronized between user systems (e.g., personal computers and sensor devices) and cloud computing servers. However, to ensure data privacy, it is necessary to encrypt sensitive data prior to outsourcing. Limitations such as measurement, network delays, and data obfuscation may, however, result in uncertain data. Compared with searching over encrypted certain data, processing queries for encrypted uncertain data is more challenging. In this article, we propose a secure and efficient range query scheme over outsourced encrypted uncertain data, for example, from Internet of Things (IoT) systems. Specifically, we use pivot mapping to map data to a low-dimensional space to facilitate calculation and processing while preserving some of the original relevance among the data. Additionally, we encode data and then map codes into multiple Bloom filters which are organized by a binary tree-based index. Our scheme achieves data privacy, hides the relevance among data, and also supports efficient queries. We analyze the security and evaluate the performance of our approach using experiments on Microsoft Azure. The analysis and experimental results demonstrate that our proposed approach is secure and efficient.

Medical imaging is crucial for medical diagnosis, and the sensitive nature of medical images necessitates rigorous security and privacy solutions to be in place. In a cloud-based medical system for Healthcare Industry 4.0, medical images should be encrypted prior to being outsourced. However, processing queries over encrypted data without first executing the decryption operation is challenging and impractical at present. In this paper, we propose a secure and efficient scheme to find the exact nearest neighbor over encrypted medical images. Instead of calculating the Euclidean distance, we reject candidates by computing the lower bound of the Euclidean distance that is related to the mean and standard deviation of data. Unlike most existing schemes, our scheme can obtain the exact nearest neighbor rather than an approximate result. We, then, evaluate our proposed approach to demonstrate its utility.

Internet of Things (IoT) is an increasingly popular technological trend. The operation of IoT needs a strong data-handling capacity, where most of the data are sensor data. Limitations associated with measurement, delays in data updating, and/or the need to preserve the privacy of data can result in the sensor data being uncertain. Thus, one key challenge is “how do we ensure the privacy of data collected from IoT devices, particularly uncertain data, that are being outsourced to the cloud for analysis, storage and archival?”. Searchable encryption scheme is a promising technique that allows the searching over encrypted (uncertain) data stored offshore. In this paper, we propose a secure range search for encrypted data from IoT devices. Specifically, we use homomorphic and order-preserving encryption to encrypt data published by the data owners. We then use the k-dimensional tree to build the data index. Our scheme is designed to ensure the privacy of the dataset, without affecting the efficiency of keyword search on the (encrypted) dataset. We also demonstrate that our scheme can preserve both data and query privacy, as well as evaluating its performance to demonstrate efficiency.