An Efficient Searchable Encryption Against Keyword Guessing Attacks for Sharable Electronic Medical Records in Cloud-based System

Specified keywords search scheme for EHR sharing

Searchable encryption allows data users to search for encrypted files by keywords without restriction. However, electronic health record (EHR) contains sensitive information, and data users should search for and share EHR with restriction. If data users are not restricted when EHR is searched and shared, there is a high risk that EHR will be misused and reveal large amounts of private patient information. This paper proposes a specified keywords search scheme for EHR sharing based on searchable encryption and proxy re-encryption to address this problem. In the scheme, the data user searches with the keywords specified by the doctor and obtains EHR from the medical cloud. Proxy re-encryption is used to implement the sharing of EHR and privacy preservation securely. The security proof demonstrates that our scheme is secure against chosen keyword attack. Furthermore, the experimental results show that the scheme achieves computational efficiency

Research on recognition and classification of pulse signal features based on EPNCC

To rapidly obtain the complete characterization information of pulse signals and to verify the sensitivity and validity of pulse signals in the clinical diagnosis of related diseases. In this paper, an improved PNCC method is proposed as a supplementary feature to enable the complete characterization of pulse signals. In this paper, the wavelet scattering method is used to extract time-domain features from impulse signals, and EEMD-based improved PNCC (EPNCC) is used to extract frequency-domain features. The time–frequency features are mixed into a convolutional neural network for final classification and recognition. The data for this study were obtained from the MIT-BIH-mimic database, which was used to verify the effectiveness of the proposed method. The experimental analysis of three types of clinical symptom pulse signals showed an accuracy of 98.3% for pulse classification and recognition. The method is effective in complete pulse characterization and improves pulse classification accuracy under the processing of the three clinical pulse signals used in the paper.

A data-sharing scheme that supports multi-keyword search for electronic medical records

As cloud storage technology develops, data sharing of cloud-based electronic medical records (EMRs) has become a hot topic in the academia and healthcare sectors. To solve the problem of secure search and sharing of EMR in cloud platforms, an EMR data-sharing scheme supporting multi-keyword search is proposed. The proposed scheme combines searchable encryption and proxy re-encryption technologies to perform keyword search and achieve secure sharing of encrypted EMR. At the same time, the scheme uses a traceable pseudo identity to protect the patient’s private information. Our scheme is proven secure based on the modified Bilinear Diffie-Hellman assumption and Quotient Decisional Bilinear Diffie-Hellman assumption under the random oracle model. The performance of our scheme is evaluated through theoretical analysis and numerical simulation.

Public Key Encryption with Keyword Search in Cloud: A Survey

With the popularization of cloud computing, many business and individuals prefer to outsource their data to cloud in encrypted form to protect data confidentiality. However, how to search over encrypted data becomes a concern for users. To address this issue, searchable encryption is a novel cryptographic primitive that enables user to search queries over encrypted data stored on an untrusted server while guaranteeing the privacy of the data. Public key encryption with keyword search (PEKS) has received a lot of attention as an important branch. In this paper, we focus on the development of PEKS in cloud by providing a comprehensive research survey. From a technological viewpoint, the existing PEKS schemes can be classified into several variants: PEKS based on public key infrastructure, PEKS based on identity-based encryption, PEKS based on attribute-based encryption, PEKS based on predicate encryption, PEKS based on certificateless encryption, and PEKS supporting proxy re-encryption. Moreover, we propose some potential applications and valuable future research directions in PEKS.

Privacy Policy and Technology in Biomedical Data Science

Privacyis an important consideration when sharing clinical data, which often contain sensitive information. Adequate protection to safeguard patient privacy and to increase public trust in biomedical research is paramount. This review covers topics in policy and technology in the context of clinical data sharing. We review policy articles related to (a) the Common Rule, HIPAA privacy and security rules, and governance; (b) patients' viewpoints and consent practices; and (c) research ethics. We identify key features of the revised Common Rule and the most notable changes since its previous version. We address data governance for research in addition to the increasing emphasis on ethical and social implications. Research ethics topics include data sharing best practices, use of data from populations of low socioeconomic status (SES), recent updates to institutional review board (IRB) processes to protect human subjects' data, and important concerns about the limitations of current policies to address data deidentification. In terms of technology, we focus on articles that have applicability in real world health care applications: deidentification methods that comply with HIPAA, data anonymization approaches to satisfy well-acknowledged issues in deidentified data, encryption methods to safeguard data analyses, and privacy-preserving predictive modeling. The first two technology topics are mostly relevant to methodologies that attempt to sanitize structured or unstructured data. The third topic includes analysis on encrypted data. The last topic includes various mechanisms to build statistical models without sharing raw data.