Mobile healthcare (mHealth) enables people to collect and share their personal health records (PHRs) and gain rapid medical treatment via mobile 5G-enabled Industrial Internet of Things (IIoT) devices, which also brings the challenge of keeping the PHRs confidentiality and preventing unauthorized access. By the emerging ciphertext-policy attribute-based encryption (CP-ABE), the PHR owner can encrypt his/her PHR data under self-defined access policies. However, existing CP-ABE schemes are suffering from either heavy computation cost and storage overhead or traitor tracing and direct revocation. In this article, we propose an efficient, traceable, and revocable access control scheme named TRAC for mHealth in 5G-enabled IIoT. In TRAC, the ciphertext is composed of the attribute-relevant ciphertext encrypted under an and -gate access structure and the identity-relevant ciphertext associated with some potential receivers. The malicious user who leaks his/her privilege to unauthorized entities will be precisely tracked and added in the revocation list, by which the cloud server can update the identity-relevant ciphertext by itself. The length of final ciphertext and the time of bilinear pairing operations used in decryption are constant. The security analysis and performance evaluation indicate the security, efficiency, and practicality of TRAC.

Federated learning (FL) has gained popularity in the field of machine learning, which allows multiple participants to collaboratively learn a highly-accurate global model without exposing their sensitive data. However, FL is susceptible to poisoning attacks, in which malicious clients manipulate local model parameters to corrupt the global model. Existing FL frameworks based on detecting malicious clients suffer from unreasonable assumptions (e.g., clean validation datasets) or fail to balance robustness and efficiency. To address these deficiencies, we propose FedDMC, which implements robust federated learning by efficiently and precisely detecting malicious clients. Specifically, FedDMC first applies principal component analysis to reduce the dimensionality of the model parameters, which retains the primary parameter feature and reduces the computational overhead for subsequent clustering. Then, a binary tree-based clustering method with noise is designed to eliminate the effect of noisy points in the clustering process, facilitating accurate and efficient malicious client detection. Finally, we design a self-ensemble detection correction module that utilizes historical results via exponential moving averages to improve the robustness of malicious client detection. Extensive experiments conducted on three benchmark datasets demonstrate that FedDMC outperforms state-of-the-art methods in terms of detection precision, global model accuracy, and computational complexity.

Federated learning (FL), a cooperative distributed learning framework, has been employed in various intelligent Internet of Things (IoT) applications (e.g., smart health-care, smart home, and smart industry). However, there may be malicious devices in these IoT applications inferring other devices’ privacy or destroying the uploaded model parameters. Besides, due to the heterogeneity of IoT devices, it is difficult for the existing synchronized FL to effectively train models through non-identical independently distributed (non-IID) local data sets. To address these issues, we propose an asynchronous grouped federated learning framework (PAG-FL) for IoT, enabling multiple devices and the server to collaboratively and efficiently train models without revealing privacy. PAG-FL framework consists of an adaptive Rényi Differential Privacy-based privacy budget allocation (ARB) protocol and an asynchronous weight-based grouped update (AWGU) algorithm. In particular, our ARB protocol applies Rényi Differential Privacy and adaptively adjusts the privacy budget to obtain an efficient local model. The AWGU algorithm can defend against the poisoning attack on non-IID data set by weighing grouped local models to generate a global model. Meanwhile, it also realizes the asynchronous optimized update by adopting a lazy loading strategy. Theoretically, the proposed framework has a convergence guarantee and a privacy guarantee when training over the non-IID data set in an asynchronous FL. Our empirical experiments validate the effectiveness of the theoretical design and demonstrate the improved utility and robustness of PAG-FL in heterogeneous IoT.

Federated learning (FL) enables collaborative model training across multiple institutions without the sharing of raw patient data, making it particularly suitable for smart healthcare applications. However, recent studies revealed that merely sharing gradients provides a false sense of security, as private information can still be inferred through gradient inversion attacks (GIAs). While differential privacy (DP) provides provable privacy guarantees, traditional DP methods apply uniform protection, leading to excessive protection for low-sensitivity data and insufficient protection for high-sensitivity data, which degrades model performance and increases privacy risks. This paper proposes a new privacy notion, sensitivity-aware differential privacy, to better balance model performance and privacy protection. Our idea is that the sensitivity of each data sample can be objectively measured using real-world attacks. To implement this new notion, we develop the corresponding defense mechanism that adjusts privacy protection levels based on the variation in the privacy leakage risks of gradient inversion attacks. Furthermore, the method extends naturally to multi-attack scenarios. Extensive experiments on real-world medical imaging datasets demonstrate that, under equivalent privacy risk, our method achieves an average performance improvement of 13.5% over state-of-the-art methods.

As an emerging cryptographic primitive, Ciphertext-Policy Attribute-Based Encryption (CP-ABE) is suitable for the owner to share his personal health records (PHRs) in mobile healthcare systems (mHealth). Before deploying traditional CP-ABE in real mHealth applications, there are three concerns worth considering. First, the scale of pre-defined attribute universe is lack of scalability. Second, the plaintext access policy sent along with the ciphertext would leak the PHR owner’s privacy. Third, it is difficult to identify the malicious user who intentionally disclosed his (partial or modified) private key. In this paper, we present HTAC, a fine-grained policy-hiding and traceable access control scheme for mHealth. In HTAC, the attribute universe is exponentially large and unbounded. Each attribute is expressed by an attribute name and an attribute value. In the encryption phase, the value is hidden in the ciphertext and only the generic attribute name is exposed. The malicious user will be precisely identified by searching the identity linked with the suspicious private key in an identity table. We further extend HTAC by removing the identity table and assigning more explicitly responsibility for the authority and the trace center. Then the storage overhead of tracing the malicious users is constant. The security analysis and performance comparison indicate that HTAC and the extended scheme are secure and practicable for real mHealth.

Dynamic Spectrum Sharing (DSS) has been taken as an encouraging method to address the increasing demand for wireless spectrum resources. Without privacy protection in location, however, the primary user will hesitate to share the spectrum with secondary users. In this paper, we present LpriDSS, the first scheme for a spectrum administrator to select spectrum-sharing secondary users while implementing the geo-indistinguishability of primary user. Firstly, we formulate the secondary users selection without privacy in DSS system as an auction problem and demonstrate the location inference attacks based on the winner sets in detail. Then, a ranking metric to characterize the administrators' preference for secondary users is defined. Moreover, a probability of each secondary user being selected as a winner is calculated and used during the process of the secondary user selection. Finally, a truthful payment computation is designed according to that probability. Thorough theoretical analysis and simulation studies show that LpriDSS can simultaneously achieve geo-indistinguishability, approximate social welfare maximization, and truthfulness.