SHFL: K-Anonymity-Based Secure Hierarchical Federated Learning Framework for Smart Healthcare Systems

Abstract

Dynamic and smart Internet of Things (IoT) infrastructures allow the development of smart healthcare systems, which are equipped with mobile health and embedded healthcare sensors to enable a broad range of healthcare applications. These IoT applications provide access to the clients’ health information. However, the rapid increase in the number of mobile devices and social networks has generated concerns regarding the secure sharing of a client’s location. In this regard, federated learning (FL) is an emerging paradigm of decentralized machine learning that guarantees the training of a shared global model without compromising the data privacy of the client. To this end, we propose a K-anonymity-based secure hierarchical federated learning (SHFL) framework for smart healthcare systems. In the proposed hierarchical FL approach, a centralized server communicates hierarchically with multiple directly and indirectly connected devices. In particular, the proposed SHFL formulates the hierarchical clusters of location-based services to achieve distributed FL. In addition, the proposed SHFL utilizes the K-anonymity method to hide the location of the cluster devices. Finally, we evaluated the performance of the proposed SHFL by configuring different hierarchical networks with multiple model architectures and datasets. The experiments validated that the proposed SHFL provides adequate generalization to enable network scalability of accurate healthcare systems without compromising the data and location privacy.

k-IPfedAvg: k-Anonymous Integrally Private Federated Averaging with Convergence Guarantee

“…For example, in [17], discriminative attributes were identified and anonymized on the local devices before syntactic learning at the server. In [18] based on the degree of privacy, each client decides the amount of data to be shared with the server, in [19] a hierarchical structure was given where the server communicated with clients who in turn have sub-clients based on the distance measure and many others. On the other hand, differential privacy [9] and its variants (such as local-DP [20], client-level DP [10]) offer privacy guarantee by adding noise.…”

Elliptic Crypt With Secured Blockchain Assisted Federated Q-Learning Framework for Smart Healthcare

“…Unevenly distributed data leads to training with low efficiency and lower accuracy. This problem can be reduced by using a privacy-preserving FL framework in fog computing [17,18].…”

A Privacy-Preserving Trajectory Publishing Method Based on Multi-Dimensional Sub-Trajectory Similarities

“…In the contemporary mobile internet era, individuals increasingly depend on mobile devices and applications for activities such as information access, social networking, and online shopping, all facilitated by the collection and utilization of location data. Furthermore, the advancements in big data, artificial intelligence, and related technologies enable the utilization of users’ location data for purposes like optimization of traffic flows [ 1 ] and advertising [ 2 ], as well as intelligent medical systems [ 3 ]. However, collecting location data also raises many security and privacy concerns.…”