A Privacy-Protection Model for Patients

Abstract

The collection and analysis of patient cases can effectively help researchers to extract case feature and to achieve the objectives of precision medicine, but it may cause privacy issues for patients. Although encryption is a good way to protect privacy, it is not conducive to the sharing and analysis of medical cases. In order to address this problem, this paper proposes a federated learning verification model, which combines blockchain technology, homomorphic encryption, and federated learning technology to effectively solve privacy issues. Moreover, we present a FL-EM-GMM Algorithm (Federated Learning Expectation Maximization Gaussian Mixture Model Algorithm), which can make model training without data exchange for protecting patient’s privacy. Finally, we conducted experiments on the federated task of datasets from two organizations in our model system, where the data has the same sample ID with different subset features, and this system is capable of handling privacy and security issues. The results show that the model was trained by our system with better usability, security, and higher efficiency, which is compared with the model trained by traditional machine learning methods.

Real-Time Collection of Training Distance of Long-Distance Runners Based on Wireless Sensor Network

“…However, the extensive users' data processing from the IoT device brings some privacy problems [23]. As the IoTdevices can be deeply involved in users' private data, the data generated by them will contain privacy-sensitive information [24]. To tackle the privacy challenges and encourage clients to proactively participate in IoT services, federated learning enables training a deep learning model across different participants in a collaborative manner.…”

Homomorphic Encryption Based Privacy-Preservation for IoMT

“…We observe from the summary in Table 1 that existing research either do not or only partially satisfy the key areas of consideration for a complete privacy-preserving scheme for securing IoMT data in a healthcare system. Implementation of homomorphic encryption [21][22][23][24] ensures data privacy stored in local edge nodes and cloud environments. Lin et al [21] performed a hypothesis test to ascertain the reliability of collected data and encrypts each set of grouped information for user privacy at the data fusion center.…”

“…Similarly, in [23][24][25][26], the objective of the research and evaluation results focuses on encrypting data for privacy but does not provide masking for computation operations from untrusted service providers such as cloud operators, IoT devices and mobile edge datacenters. Our proposed scheme benefits from secret nodes at the local edge layer, preventing untrusted cloud providers from learning the computation operations.…”

“…Raw data is uploaded to an external and private server on the Cloud for security, but the proposed scheme fails to protect it in transmission. Cheng et al [23] presented a patient privacy protection model based on federated learning verification. The model combines homomorphic encryption, blockchain, and federated learning to ensure the privacy of medical records as data learning is performed both centrally and locally.…”

Bibliometric Analysis of the Blockchain Scientific Evolution: 2014–2020

“…An analysis of the leading research fields shows that computer science is still the most popular research direction, and researchers are more interested in computer science, information systems [70][71][72][73][74], engineering electrical & electronic [75][76][77], and telecommunications [78][79][80].…”