Privacy-Preserving Social Media with a Disclosure

Social Media are an important communication tool in today's society. In recent years, many events have been held online due to COVID-19, making Social Media an even more important communication tool. However, it is difficult to explicitly imagine the recipients of messages when posting on Social Media and there is a tendency to provide information easily, leading to the existence of inappropriate postings that the user does not intend. Furthermore, it is difficult to disclose information for anonymous posting on Twitter. This cause the link problem between the posts. In our proposal, we realize a way to solve these problems by realizing a Social Media that allows both unlinkable posting and disclose posting. Specifically, unlinkable posts can be changed to named posts, and when the name is changed, it is guaranteed that the person who posted the anonymous post was really the anonymous writer and that the anonymous writer cannot be identified from the anonymous post. We introduced randomized pseudonyms to prevent the viewer from checking a post text based only on the posting name without checking the contents of the posting. We also show how to prevent the attack on our proposed scheme by using hiding property and binding property of the commitment scheme. In addition, we implement the proposed scheme and describe the changes between our proposed scheme and regular post in posting time, publication time, and verification time.

Blockchain-Based Trusted Federated Learning with Pre-Trained Models for COVID-19 Detection

COVID-19 is a serious epidemic that not only endangers human health, but also wreaks havoc on the development of society. Recently, there has been research on using artificial intelligence (AI) techniques for COVID-19 detection. As AI has entered the era of big models, deep learning methods based on pre-trained models (PTMs) have become a focus of industrial applications. Federated learning (FL) enables the union of geographically isolated data, which can address the demands of big data for PTMs. However, the incompleteness of the healthcare system and the untrusted distribution of medical data make FL participants unreliable, and medical data also has strong privacy protection requirements. Our research aims to improve training efficiency and global model accuracy using PTMs for training in FL, reducing computation and communication. Meanwhile, we provide a secure aggregation rule using differential privacy and fully homomorphic encryption to achieve a privacy-preserving Byzantine robust federal learning scheme. In addition, we use blockchain to record the training process and we integrate a Byzantine fault tolerance consensus to further improve robustness. Finally, we conduct experiments on a publicly available dataset, and the experimental results show that our scheme is effective with privacy-preserving and robustness. The final trained models achieve better performance on the positive prediction and severe prediction tasks, with an accuracy of 85.00% and 85.06%, respectively. Thus, this indicates that our study is able to provide reliable results for COVID-19 detection.

Privacy preserving via multi-key homomorphic encryption in cloud computing

As the world grapples with the COVID-19 and its variants, multi-user collaboration by means of cloud computing is ubiquitous. How to make better use of cloud resources while preventing user privacy leakage has become particularly important. Multi-key homomorphic encryption(MKHE) can effectively deal with the privacy disclosure issue during the multi-user collaboration in the cloud computing setting. Firstly, we improve the DGHV homomorphic scheme by modifying the selection of key and the coefficients in encryption, so as to eliminate the restriction on the parity of the ciphertext modulus in the public key. On this basis, we further propose a DGHV-type MKHE scheme based on the number theory. In our scheme, an extended key is introduced for ciphertext extension, and we prove that it is efficient in performance analysis. The semantic security of our schemes is proved under the assumption of error-free approximate greatest common divisor and the difficulty of large integer factorization. Furthermore, the simulation experiments show the availability and computational efficiency of our MKHE scheme. Therefore, our scheme is suitable for the multi-user scenario in cloud environment. © 2023 Elsevier Ltd

Multi-party Secure Comparison of Strings Based on Outsourced Computation

Data is an important production factor in the era of digital economy. Privacy computing can ensure that data providers do not disclose sensitive data, carry out multi-party joint analysis and computation, securely and privately complete the full excavation of data value in the process of circulation, sharing, fusion, and calculation, which has become a popular research topic. String comparison is one of the common operations in data processing. To address the string comparison problem in multi-party scenarios, we propose an algorithm for secure string comparison based on outsourced computation. The algorithm encodes the strings with one hot encoding scheme and encrypts the encoded strings using an XOR homomorphic encryption scheme. The proposed algorithm achieves efficient and secure string comparison and counts the number of different characters with the help of a cloud-assisted server. The proposed scheme is implemented and verified using the new coronavirus gene sequence as the comparison string, and the performance is compared with that of a state-of-the-art security framework. Experiments show that the proposed algorithm can effectively improve the string comparison speed and obtain correct comparison results without compromising data privacy. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

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.

A Privacy-Preserving Gait Recognition Scheme Under Homomorphic Encryption

In recent years, machine learning and deep neural networks have achieved remarkable results and have been widely used in different domains. Affected by COVID-19, the potential of gait feature recognition in biometric authentication has gradually emerged. However, machine learning algorithms are generally demanded in terms of computing power, sometimes need the support of cloud service providers, and require raw data, which is often sensitive, most privacy-preserving approaches only encrypted the trained model, and the data collected from users are unprotected. We propose a scheme for running deep neural networks on encrypted data using homomorphic encryption to address these issues. © 2022 IEEE.

Towards Privacy-Preserving Computation on Gene: Construct Covid-19 Phylogenetic Tree using Homomorphic Encryption

Constructing a phylogenetic tree is an essential method of analyzing the evolution of the covid-19 virus. In the case of multiple entities holding different coronavirus genetic data, it is simple to aggregate all data into one entity and then calculate the phylogenetic tree. However, such a method is challenging to carry out. Genetic data is susceptible and has high economic value, and it is usually impossible to copy between different entities directly. Also, the direct sharing of genetic data can lead to data leaks or even legal problems. In this paper, we propose a homomorphic-encryption-based solution to tackle this problem, where two participants, A and B, both hold a part of covid-19 genetic data and compute the gene distance matrix calculation of the overall dataset without revealing the genetic data held by both parties. After the computation, participant A can decrypt the final distance matrix from the encrypted result and then use the plain-text result to construct the covid-19 phylogenetic tree. Experiment results show that the proposed method can process the genetic data accurately in a short time, and the phylogenetic tree generated by the proposed solution has no loss of accuracy compared to plain-text calculation. In terms of engineering optimization, we propose an optimized encryption method, which can further shorten the encryption time of the entire dataset without reducing the security level. © 2022 IEEE.

A Methodology for Training Homomorphic Encryption Friendly Neural Networks

Privacy-preserving deep neural network (DNN) inference is a necessity in different regulated industries such as healthcare, finance, and retail. Recently, homomorphic encryption (HE) has been used as a method to enable analytics while addressing privacy concerns. HE enables secure predictions over encrypted data. However, there are several challenges related to the use of HE, including DNN size limitations and the lack of support for some operation types. Most notably, the commonly used ReLU activation is not supported under some HE schemes. We propose a structured methodology to replace ReLU with a quadratic polynomial activation. To address the accuracy degradation issue, we use a pre-trained model that trains another HE-friendly model, using techniques such as 'trainable activation' functions and knowledge distillation. We demonstrate our methodology on the AlexNet architecture, using the chest X-Ray and CT datasets for COVID-19 detection. Experiments using our approach reduced the gap between the F-1 score and accuracy of the models trained with ReLU and the HE-friendly model to within a mere 0.32-5.3% degradation. We also demonstrate our methodology using the SqueezeNet architecture, for which we observed 7% accuracy and F-1 improvements over training similar networks with other HE-friendly training methods.

Securing the Classification of COVID-19 in Chest X-ray Images: A Privacy-Preserving Deep Learning Approach

Deep learning (DL) is being increasingly utilized in healthcare-related fields due to its outstanding efficiency. However, we have to keep the individual health data used by DL models private and secure. Protecting data and preserving the privacy of individuals has become an increasingly prevalent issue. The gap between the DL and privacy communities must be bridged. In this paper, we propose privacy-preserving deep learning (PPDL)-based approach to secure the classification of Chest X-ray images. This study aims to use Chest X-ray images to their fullest potential without compromising the privacy of the data that it contains. The proposed approach is based on two steps: encrypting the dataset using partially homomorphic encryption and training/testing the DL algorithm over the encrypted images. Experimental results on the COVID-19 Radiography database show that the MobileNetV2 model achieves an accuracy of 94.2% over the plain data and 93.3% over the encrypted data. © 2022 IEEE.

Homomorphic Encryption and Federated Learning based Privacy-Preserving CNN Training: COVID-19 Detection Use-Case

Medical data is often highly sensitive in terms of data privacy and security concerns. Federated learning, one type of machine learning techniques, has been started to use for the improvement of the privacy and security of medical data. In the federated learning, the training data is distributed across multiple machines, and the learning process is performed in a collaborative manner. There are several privacy attacks on deep learning (DL) models to get the sensitive information by attackers. Therefore, the DL model itself should be protected from the adversarial attack, especially for applications using medical data. One of the solutions for this problem is homomorphic encryption-based model protection from the adversary collaborator. This paper proposes a privacy-preserving federated learning algorithm for medical data using homomorphic encryption. The proposed algorithm uses a secure multi-party computation protocol to protect the deep learning model from the adversaries. In this study, the proposed algorithm using a real-world medical dataset is evaluated in terms of the model performance. © 2022 ACM.

Privacy-Oriented Technique for COVID-19 Contact Tracing (PROTECT) Using Homomorphic Encryption: Design and Development Study.

BACKGROUND: Various techniques are used to support contact tracing, which has been shown to be highly effective against the COVID-19 pandemic. To apply the technology, either quarantine authorities should provide the location history of patients with COVID-19, or all users should provide their own location history. This inevitably exposes either the patient's location history or the personal location history of other users. Thus, a privacy issue arises where the public good (via information release) comes in conflict with privacy exposure risks. OBJECTIVE: The objective of this study is to develop an effective contact tracing system that does not expose the location information of the patient with COVID-19 to other users of the system, or the location information of the users to the quarantine authorities. METHODS: We propose a new protocol called PRivacy Oriented Technique for Epidemic Contact Tracing (PROTECT) that securely shares location information of patients with users by using the Brakerski/Fan-Vercauteren homomorphic encryption scheme, along with a new, secure proximity computation method. RESULTS: We developed a mobile app for the end-user and a web service for the quarantine authorities by applying the proposed method, and we verified their effectiveness. The proposed app and web service compute the existence of intersections between the encrypted location history of patients with COVID-19 released by the quarantine authorities and that of the user saved on the user's local device. We also found that this contact tracing smartphone app can identify whether the user has been in contact with such patients within a reasonable time. CONCLUSIONS: This newly developed method for contact tracing shares location information by using homomorphic encryption, without exposing the location information of patients with COVID-19 and other users. Homomorphic encryption is challenging to apply to practical issues despite its high security value. In this study, however, we have designed a system using the Brakerski/Fan-Vercauteren scheme that is applicable to a reasonable size and developed it to an operable format. The developed app and web service can help contact tracing for not only the COVID-19 pandemic but also other epidemics.

Secure and Provenance Enhanced Internet of Health Things Framework: A Blockchain Managed Federated Learning Approach.

Recent advancements in the Internet of Health Things (IoHT) have ushered in the wide adoption of IoT devices in our daily health management. For IoHT data to be acceptable by stakeholders, applications that incorporate the IoHT must have a provision for data provenance, in addition to the accuracy, security, integrity, and quality of data. To protect the privacy and security of IoHT data, federated learning (FL) and differential privacy (DP) have been proposed, where private IoHT data can be trained at the owner's premises. Recent advancements in hardware GPUs even allow the FL process within smartphone or edge devices having the IoHT attached to their edge nodes. Although some of the privacy concerns of IoHT data are addressed by FL, fully decentralized FL is still a challenge due to the lack of training capability at all federated nodes, the scarcity of high-quality training datasets, the provenance of training data, and the authentication required for each FL node. In this paper, we present a lightweight hybrid FL framework in which blockchain smart contracts manage the edge training plan, trust management, and authentication of participating federated nodes, the distribution of global or locally trained models, the reputation of edge nodes and their uploaded datasets or models. The framework also supports the full encryption of a dataset, the model training, and the inferencing process. Each federated edge node performs additive encryption, while the blockchain uses multiplicative encryption to aggregate the updated model parameters. To support the full privacy and anonymization of the IoHT data, the framework supports lightweight DP. This framework was tested with several deep learning applications designed for clinical trials with COVID-19 patients. We present here the detailed design, implementation, and test results, which demonstrate strong potential for wider adoption of IoHT-based health management in a secure way.