Towards Secure and Privacy-Preserving Data Sharing for COVID-19 Medical Records: A Blockchain-Empowered Approach

COVID-19 is currently a major global public health challenge. In the battle against the outbreak of COVID-19, how to manage and share the COVID-19 Electric Medical Records (CEMRs) safely and effectively in the world, prevent malicious users from tampering with CEMRs, and protect the privacy of patients are very worthy of attention. In particular, the semi-trusted medical cloud platform has become the primary means of hospital medical data management and information services. Security and privacy issues in the medical cloud platform are more prominent and should be addressed with priority. To address these issues, on the basis of ciphertext policy attribute-based encryption, we propose a blockchain-empowered security and privacy protection scheme with traceable and direct revocation for COVID-19 medical records. In this scheme, we perform the blockchain for uniform identity authentication and all public keys, revocation lists, etc are stored on a blockchain. The system manager server is responsible for generating the system parameters and publishes the private keys for the COVID-19 medical practitioners and users. The cloud service provider (CSP) stores the CEMRs and generates the intermediate decryption parameters using policy matching. The user can calculate the decryption key if the user has private keys and intermediate decrypt parameters. Only when attributes are satisfied access policy and the user's identity is out of the revocation list, the user can get the intermediate parameters by CSP. The malicious users may track according to the tracking list and can be directly revoked. The security analysis demonstrates that the proposed scheme is indicated to be safe under the Decision Bilinear Diffie-Hellman (DBDH) assumption and can resist many attacks. The simulation experiment demonstrates that the communication and storage overhead is less than other schemes in the public-private key generation, CEMRs encryption, and decryption stages. Besides, we also verify that the proposed scheme works well in the blockchain in terms of both throughput and delay.

Privacy Protection and Regulatory Aspects in the context of Medical Apps

Due to the COVID-19 pandemic, there has been a significant increase in the development of medical apps worldwide in recent years, both in research projects and in industry. However, unfortunately the development of such apps has often been significantly slowed down, if not stopped, due to bureaucratic problems frequently related to privacy. Therefore, in this paper we aim to summarize regulatory aspects and privacy protection in the context of medical apps, in order to provide suggestions and guidelines for app designers and developers. © 2022 IEEE.

Efficient Online and Privacy-preserving Medical Pre-diagnosis Based on Growing Learning Vector Quantization

With more and more people turning to online medical pre-diagnosis systems, it becomes increasingly important to protect patient privacy and enhance the accuracy and efficiency of diagnosis. That is because the ever rapidly growing medical records not only contain a large amount of private information but are often highly unequally distributed (e.g., the number of cases and the rate of increase of covid-19 can be much higher than that of common diseases). However, existing methods are not capable of simultaneously boosting the intensity of privacy protection, and the accuracy and efficiency of diagnosis. In this paper, we propose an online medical pre-diagnosis scheme based on incremental learning vector quantization (called WL-OMPD) to achieve the two objectives at the same time. Specifically, within WL-OMPD, we design an efficient algorithm, Wasserstein-Learning Vector Quantization (W-LVQ), to smartly compress the original medical records into hypothetic samples. Then, we transmit these compressed data to the cloud instead of the original records to offer a more accurate pre-diagnosis. Extensive evaluations of real medical datasets show that the WL-OMPD scheme can improve the imbalance ratio of the data to a certain extent and then the intensity of privacy protection. These results also demonstrate that WL-OMPD substantially boost the accuracy of the classification model and increase diagnostic efficiency at a lower compression rate. © 2022 IEEE.

The New Progress and Methods of Privacy Protection on Medical and Health Big Data*

In recent years, due to the emergence of COVID-19(Corona Virus Disease 2019), how to have a higher quality medical environment has become a troubling problem. The proposal of the Office of the State Council on promoting the development of 'Internet plus medical and health' has brought a lot of convenience to the public, but also brought about the problem of data leakage and other user privacy protection. In view of the problems of user's personal information storage and user's health data processing in the medical and health context, how to ensure that these data are not stolen, leaked or tampered with has become a major challenge faced by current researchers. Based on the privacy protection of users in the context of health care, this paper classifies the current privacy protection mechanisms, and introduces the latest progress of related technologies. Finally, according to the integrated information, the research direction of privacy protection technologies in the field of health care is prospected. © 2022 IEEE.

An Analysis of Mobile Gaming Apps' Privacy Policies

In the past decade, the gaming industry has seen a sharp rise in popularity, particularly in mobile gaming, and these numbers have only increased with the recent COVID-19 pandemic. Given the amount of user information being collected and shared by these gaming apps as well as the demographics of its users such as minors, it is critical to examine these apps' privacy vulnerabilities. In this study, we reviewed and analyzed 20 popular gaming apps' privacy policies and evaluated their explicit privacy protections or lack thereof. In particular, we examined if any specific privacy protections are provided to vulnerable groups like children and teenagers. Results found that although these gaming apps have privacy protections listed in their policies, only a few of them explicitly identify individual's consent and choice. Also, most of the privacy protections on minors like children and teenagers provided by these gaming apps are only at a basic level. Results from this study can provide guidance to both app users and app developers on the measures that each app is already taking on privacy protections, as well as identifying the vulnerabilities and potential privacy risks that currently exist. Furthermore, it can provide guidance for implementing appropriate privacy policies to protect users' personal data. © 2022 IEEE.

Asymmetric Differential Privacy

Differential privacy (DP) is attracting considerable research attention as a privacy definition when publishing statistics of a dataset. This study focused on addressing the limitation that DP inevitably causes two-sided errors. For example, consider a threshold query that asks whether a counting is above a given threshold or not. An answer through the DP mechanism can cause error. This phenomenon is not desirable for sensitive analysis such as the counting of COVID-19-infected individuals (in a dataset) visiting a specific location;misinformation can result in incorrect decision-making which can increase the epidemic. To the best of our knowledge, the problem is yet to be solved. We proposed a variation of DP, namely asymmetric DP (ADP) to solve the problem. ADP can provide reasonable privacy protection and achieve one-sided errors. Finally, experiments were conducted to evaluate the utility of the proposed mechanism for the epidemic analysis using a real-world dataset. The results of study revealed the feasibility of proposed mechanisms. © 2022 IEEE.

PrivacyMask: Real-world privacy protection in face ID systems.

Recent works have illustrated that many facial privacy protection methods are effective in specific face recognition algorithms. However, the COVID-19 pandemic has promoted the rapid innovation of face recognition algorithms for face occlusion, especially for the face wearing a mask. It is tricky to avoid being tracked by artificial intelligence only through ordinary props because many facial feature extractors can determine the ID only through a tiny local feature. Therefore, the ubiquitous high-precision camera makes privacy protection worrying. In this paper, we establish an attack method directed against liveness detection. A mask printed with a textured pattern is proposed, which can resist the face extractor optimized for face occlusion. We focus on studying the attack efficiency in adversarial patches mapping from two-dimensional to three-dimensional space. Specifically, we investigate a projection network for the mask structure. It can convert the patches to fit perfectly on the mask. Even if it is deformed, rotated and the lighting changes, it will reduce the recognition ability of the face extractor. The experimental results show that the proposed method can integrate multiple types of face recognition algorithms without significantly reducing the training performance. If we combine it with the static protection method, people can prevent face data from being collected.

Ethical Governance of Personal Privacy Protection in the Prevention and Control of Major Infectious Diseases

The outbreak of major infectious diseases such as COVID-19 are unpredictable. In order to prevent the rapid spread of the epidemic, it is necessary to quickly start the first-class response to public health emergencies, take prevention and control measures such as isolating confirmed patients, suspected cases and close contacts, tracking their activity tracks, and publishing their infection related information, which may cause the leakage of personal privacy and information. Take preventive and control measures, which needs to protect the public interests while taking into account individual rights and interests, including privacy protection, and obtaining public understanding and support. The ethical governance of personal privacy protection in the prevention and control of major infectious diseases needs to regulate the use of personal information according to laws and regulations, achieve effective ethical governance in multiple dimensions, establish and improve the supervision and management mechanism of personal privacy protection, enhance the privacy protection awareness of relevant departments and staff, increase the punishment for illegal acts, strengthen science popularization, promote public understanding, and improve the efficiency and effectiveness of prevention and control. © 2022, Chinese Medical Ethics. All rights reserved.

COVID-LPS: Location-Protected Services for COVID-19 Prevention

The COVID-19 pandemic has caused not only worldwide health problems but also economic damage. Numerous researchers and intuitions have attempted to visualize confirmed COVID-19 cases with maps to provide timely information to users (e.g., warnings upon entry of crowded areas) and prevent the spread of COVID-19. However, such systems are limited by their poor protection of private information because they must collect sensitive information, such as the locations of individuals. We propose a practical method of obtaining a distribution of users while anonymizing their location data that can be used in location-based services for the prevention of the spread of COVID-19. Generalization and local differential privacy are used to guarantee user and data anonymity while maintaining high data utility and accuracy. To our knowledge, COVID-LPS is not only the first COVID-19 tracing system in Taiwan but also the first system to visualize user distributions for location-based services while protecting user privacy through generalization and local differential privacy. © 2022 IEEE.

Improved Gentry–Halevi's fully homomorphic encryption-based lightweight privacy preserving scheme for securing medical Internet of Things

Internet of Medical Things (IoMT) solutions have proliferated rapidly in the COVID-19 pandemic era. The smart medical sensors capture real-time data from remote patients and communicate it to medical servers in a secure and privacy-preserving manner. It is a herculean challenge to guarantee security and privacy in Medical IoT applications. Hence, an improved Gentry–Halevi's fully homomorphic encryption-based (IGHFHE) lightweight privacy preserving user authentication scheme is proposed in this work. The scheme is proposed with an integer matrix computation strategy for securing data computation with privacy protection. It adopts the translation process of Gentry–Halevi's fully homomorphic encryption process for performing homomorphic addition and multiplication, then encrypt an integer matrix modulo that represents a positive integer. Extensive informal investigation and simulation of the proposed IGHFHE scheme shows that it is more resistant to well-known attacks for preventing authentication breaches. Also, the proposed IGHFHE scheme reduced computational and storage overhead by 4.98% and 5.78% respectively on average in comparison to other prevailing schemes. © 2023 John Wiley & Sons Ltd.

AQRS: Anti-quantum ring signature scheme for secure epidemic control with blockchain.

Epidemics, such as Corona Virus Disease 2019 (COVID-19), have serious consequences globally, of which the most effective way to control the infection is contact tracing. Nowadays, research related to privacy-preserving epidemic infection control has been conducted, nevertheless, current researchers do not regard the authenticity of records and infection facts as well as poor traceability. Moreover, with the emergence of quantum computing, there is a bottleneck in upholding privacy, security and efficiency. Our paper proposes a privacy-preserving epidemic infection control scheme through lattice-based linkable ring signature in blockchain, called AQRS. Firstly, our scheme adopts a blockchain with three ledgers to store information in a distributed manner, which offers transparency and immunity from the Single Point of Failure (SPoF) and Denial of Service (DoS) attacks. Moreover, we design a lattice-based linkable ring signature scheme to secure privacy-preserving of epidemic infection control. Significantly, we are the first to introduce the lattice-based linkable ring signature into privacy preserving in epidemic control scenario. Security analysis indicates that our scheme ensures unconditional users anonymity, record unforgeability, signature linkability, link non-slanderability and contact traceability. Finally, the comprehensive performance evaluation demonstrates that our scheme has an efficient time-consuming, storage consumption and system communication overhead and is practical for epidemic and future pandemic privacy-preserving.

Digitalisation in the health sector: A south african public law perspective

The landscape of the health sector in South Africa as seen from a regulatory perspective is rapidly changing under the disruptive impact of digitalisation. Drawing on a paradigm of "strong rights" protection, particularly a robust privacy law fit for the digital age and sourced in the nation's Constitution, the operationalisation and application of health privacy regulation in post-apartheid society is briefly described. The note then enumerates and assesses several specific digital health technologies currently in use in interventions in South Africa. To do so, we adopt the international World Health Organisation (WHO) classification of digital health interventions. We also cover the recent South African response to the COVID-19 pandemic, noting the establishment in South Africa of the COVID-19 Tracing Database and subsequent technological interventions aimed at enhancing contact tracing and other responses to the pandemic. The establishment of the initial database was a development at the interface of the law enforcement and health sectors, which raised concerns regarding its risks to privacy, but it also raised hopes regarding its potential rewards in protecting public health. © 2022, North-West Unversity. All rights reserved.

Privacy at risk? Understanding the perceived privacy protection of health code apps in China

As a key constituent of China's approach to fighting COVID-19, Health Code apps (HCAs) not only serve the pandemic control imperatives but also exercise the agency of digital surveillance. As such, HCAs pave a new avenue for ongoing discussions on contact tracing solutions and privacy amid the global pandemic. This article attends to the perceived privacy protection among HCA users via the lens of the contextual integrity theory. Drawing on an online survey of adult HCA users in Wuhan and Hangzhou (N = 1551), we find users’ perceived convenience, attention towards privacy policy, trust in government, and acceptance of government purposes regarding HCA data management are significant contributors to users’ perceived privacy protection in using the apps. By contrast, users’ frequency of mobile privacy protection behaviors has limited influence, and their degrees of perceived protection do not vary by sociodemographic status. These findings shed new light on China's distinctive approach to pandemic control with respect to the state's expansion of big data-driven surveillance capacity. Also, the findings foreground the heuristic value of contextual integrity theory to examine controversial digital surveillance in non-Western contexts. Put tougher, our findings contribute to the thriving scholarly conversations around digital privacy and surveillance in China, as well as contact tracing solutions and privacy amid the global pandemic. © The Author(s) 2022.

A Study on Guaranteed Privacy using One-time Identification Code on Digital Health Keeper

Recently, epidemiological investigation technology for identifying infected persons based on smart phone location data has been used to prevent threats by quickly finding close contacts who may be in the early stage of infection. In addition, in order to prevent the spread of COVID-19, the technology is used for rehabilitation through video call-based EEG, ECG, and EMG sensor-based treatment support, thereby preventing close contacts with infected people and protecting medical staff and patients. In most cases, there exists security and privacy concerns. This paper studies pseudonymization to protect security and personal privacy. The core of the technology proposal to enhance security and privacy in the loT and sensing-based medical technology environment is to approach the NFC network tagging-based OTAC authentication technology from a completely different perspective. This paper suggests a new service direction that can be used in the development of a system that protects personal security and personal information. The proposed technology is valuable to security and privacy. © 2022 IEEE.

GOCPT: Generalized Online Canonical Polyadic Tensor Factorization and Completion

Low-rank tensor factorization or completion is well-studied and applied in various online settings, such as online tensor factorization (where the temporal mode grows) and online tensor completion (where incomplete slices arrive gradually). However, in many real-world settings, tensors may have more complex evolving patterns: (i) one or more modes can grow;(ii) missing entries may be filled;(iii) existing tensor elements can change. Existing methods cannot support such complex scenarios. To fill the gap, this paper proposes a Generalized Online Canonical Polyadic (CP) Tensor factorization and completion framework (named GOCPT) for this general setting, where we maintain the CP structure of such dynamic tensors during the evolution. We show that existing online tensor factorization and completion setups can be unified under the GOCPT framework. Furthermore, we propose a variant, named GOCPTE, to deal with cases where historical tensor elements are unavailable (e.g., privacy protection), which achieves similar fitness as GOCPT but with much less computational cost. Experimental results demonstrate that our GOCPT can improve fitness by up to 2.8% on the JHU Covid data and 9.2% on a proprietary patient claim dataset over baselines. Our variant GOCPTE shows up to 1.2% and 5.5% fitness improvement on two datasets with about 20% speedup compared to the best model. © 2022 International Joint Conferences on Artificial Intelligence. All rights reserved.

COVID-19 and big data technologies: Experience in China

The experience of COVID-19 has shown that big data combined with advanced algorithms have a huge potential in supporting the fight against infectious diseases and pandemics. In China, big data on human mobility derived from smart sensors, integrated with detailed epidemiological data from patient interviews, have played an important role in the efficient and effective control of the pandemic via nonpharmaceutical interventions. Two official big data applications, namely “Health Code” and “Instrument for Measuring Close Contacts,” have been promoted to detect infected people with the potential to infect and conduct risk assessments in a timely manner during the pandemic. We explored the relationship between big data technologies and applications in virus transmission, risk assessment, and recovery decision making. In the future, the process of social recovery is likely to require the support of big data technology. The experience of using big data in China is expected to bring new insights into policymaking to control the COVID-19 pandemic in other countries and prevent future pandemics.

Labeling Privacy Protection SVM Using Privileged Information for COVID-19 Diagnosis

Edge/fog computing works at the local area network level or devices connected to the sensor or the gateway close to the sensor. These nodes are located in different degrees of proximity to the user, while the data processing and storage are distributed among multiple nodes. In healthcare applications in the Internet of things, when data is transmitted through insecure channels, its privacy and security are the main issues. In recent years, learning from label proportion methods, represented by inverse calibration (InvCal) method, have tried to predict the class label based on class label proportions in certain groups. For privacy protection, the class label of the sample is often sensitive and invisible. As a compromise, only the proportion of class labels in certain groups can be used in these methods. However, due to their weak labeling scheme, their classification performance is often unsatisfactory. In this article, a labeling privacy protection support vector machine using privileged information, called LPP-SVM-PI, is proposed to promote the accuracy of the classifier in infectious disease diagnosis. Based on the framework of the InvCal method, besides using the proportion information of the class label, the idea of learning using privileged information is also introduced to capture the additional information of groups. The slack variables in LPP-SVM-PI are represented as correcting function and projected into the correcting space so that the hidden information of training samples in groups is captured by relaxing the constraints of the classification model. The solution of LPP-SVM-PI can be transformed into a classic quadratic programming problem. The experimental dataset is collected from the Coronavirus disease 2019 (COVID-19) transcription polymerase chain reaction at Hospital Israelita Albert Einstein in Brazil. In the experiment, LPP-SVM-PI is efficiently applied for COVID-19 diagnosis. © 2021 Association for Computing Machinery.

A Survey of Dummy-Based Location Privacy Protection Techniques for Location-Based Services.

As smart devices and mobile positioning technologies improve, location-based services (LBS) have grown in popularity. The LBS environment provides considerable convenience to users, but it also poses a significant threat to their privacy. A large number of research works have emerged to protect users' privacy. Dummy-based location privacy protection solutions have been widely adopted for their simplicity and enhanced privacy protection results, but there are few reviews on dummy-based location privacy protection. Or, for existing works, some focus on aspects of cryptography, anonymity, or other comprehensive reviews that do not provide enough reviews on dummy-based privacy protection. In this paper, the authors provide a review of dummy-based location privacy protection techniques for location-based services. More specifically, the connection between the level of privacy protection, the quality of service, and the system overhead is summarized. The difference and connection between various location privacy protection techniques are also described. The dummy-based attack models are presented. Then, the algorithms for dummy location selection are analyzed and evaluated. Finally, we thoroughly evaluate different dummy location selection methods and arrive at a highly useful evaluation result. This result is valuable both to users and researchers who are studying this field.

Increasing Safety and Privacy Against COVID-19 using IoTs and Enhanced Positioning Methods

This paper introduces and demonstrates a new approach to enhance safety against COVID-19, or other dangerous and contagious diseases, on mainly indoor public spaces, using enhanced privacy protection and an enhanced localization techniques. In most of the existing COVID-19 tracing systems and mobile apps, the focus is on identifying possible infected individuals, that were closed to a human source of transmission. This work includes primarily results and demonstrates a mobile app prototype and a corresponding support system to identify unsafe 'COVID-19 areas', from where infected individuals have recently crossed, so these spots to be avoided by individuals, until they will be characterized again as open to be used. © 2022 IEEE.