SENSH: a blockchain-based searchable encrypted data sharing scheme in smart healthcare

With the features of decentralization and trustlessness and through distributed data storage, point-to-point transmission, and encryption algorithms, blockchain has shed new light on the security and protection of medical data, and it can resolve the contradiction between data sharing and privacy protection with proper security strategies. In this paper, we integrate the strengths of both blockchain and cloud computing and build the privacy protection scheme for medical data based on blockchain and cloud computing. This scheme introduces cloud computing and provides services to blockchain nodes with cloud server computing; meanwhile, it collects, analyzes, processes, and maintains medical data in the identity authentication interface and solves the insufficient computing abilities of some nodes in blockchain so as to verify the authenticity and reliability of data. The simulation experiment proves that the proposed scheme is effective. It can achieve the secure protection and integrity verification of medical data and address the problems of high computing complexity, data sharing, and privacy protection.

The maintenance and sharing of electronic medical records are one of the essential tasks in the medical treatment combination. Traditional cloud-based electronic medical record storage system is difficult to realize data security sharing. The tamper resistance and traceability of blockchain technology provide the possibility for the sharing of highly sensitive medical data. This paper proposes a safe sharing scheme of stroke electronic medical records based on the consortium blockchain. The scheme adopts the storage method of ciphertext of medical records stored in the cloud and index of medical records stored on the blockchain. The privacy protection mechanism proposed in this paper innovatively combines proxy reencryption and searchable encryption which supports patient pseudoidentity search. The mechanism could achieve controllable sharing of medical records and precise search. According to the organizational characteristics of the stroke medical treatment combination, this paper proposes an improved Practical Byzantine Fault Tolerance mechanism to reach a consensus between consensus nodes. Then, the proposed scheme is analyzed and evaluated from three aspects of medical record integrity, user privacy, and data security. The results show that the scheme can not only ensure the privacy of patient identity information and private key data but also resist the tampering and deletion attacks of internal and external malicious nodes on the medical record data. Therefore, the proposed scheme is conducive to the improvement of the timeliness of stroke treatment and the safe sharing of electronic medical records in stroke medical treatment combination.

As DDoS (distributed denial of service) attack becomes more diversified, the conventional detection methods based on single source router can't detect the attack efficiently. In order to combat this problem, centralized control is required to analyze and collect traffic generated in several source routers. This paper presents defense/detection scenario to protect against DDoS attack in centralized control network. A destination address monitoring scheme is also proposed to detect DDoS attacks in real-time. It measures the number of packets with same destination IP address by using modified Bloom filter. Because the modified Bloom filter uses extra table that manages relation among each address fields of destination IP address, it can reduce wrong detection rate. Simulation result shows the proposed scheme reduces the wrong detection rate than the conventional one

With the development of medical digitization, smart medical care and medical big data technologies, secure storage and sharing of medical data have become the hotspot of research in the field of medical technology. However, Fragmentation of medical data is much more serious, and many data becomes data islands that are difficult to apply to medical research. At the same time, the centralized control of medical data by third-party institutions has security bottlenecks and performance bottlenecks, which also lead to frequent data leakage incidents. To solve this problem, we propose a medical health data sharing scheme based on blockchain and attribute-based encryption. The model implements decentralized, tamper-proof, traceable data sharing based on a distributed blockchain system. And an attribute-based encryption is used to protect the privacy of patient's medical data. The cryptographic method is used to implement fine-grained access control of the data. The model can reduce the cost of data sharing and improve the efficiency of data utilization while protecting the data security. Finally, compared with the existing medical data sharing methods, our model has certain advantages and good scalability in some aspects.

Investigating the literature reveals the fact that the key management protocols play a vital role in protecting the security and privacy of medical data in telecare medical information systems. Recently, Tseng et al . have proposed an interesting elliptic curve cryptosystem (ECC) based self-certified key management scheme that can yield secure channel for the communications of secure sensors (cluster members) and access point (cluster head). After careful consideration, we found that their scheme suffers from the cluster head impersonation, replay, and key replicating attacks. In addition, in the secure session phase, there are some errata and a point multiplication that is undefined in the ECC and hence is mathematically incorrect. Finally, yet importantly, their presented scheme has been adopted for a similar application by other authors with the same problem. Therefore, in this paper, we first try to elaborate the existing errata and security threats. Second, we propose a modified version, which is free from the challenges of their scheme. Eventually, we propose a novel anonymous ECC-based self-certified two-factor key management scheme that not only provides the desired security features, but also has much better efficiency than several recently-published schemes, such as the presented one by Tseng et al . Our formal security verification and proof besides the efficiency analysis support our claim.

Wireless data communications commonly use public networks to exchange data packets. Public networks of rural area are lack of network infrastructure so it will inflict high delay for wireless communications. The rural areas are same with intermittent area that need a mechanism of data transfer to tolerate with delay. The Delay Tolerant Network (DTN) is a solution for data transmission in rural areas that have coverless TCP/IP network. DTN uses store-and-forward mechanism to send data packets from the source to the destination through intermediary nodes. DTN uses dynamic routing protocols to determine routes of data packets delivery. DTN implements a reliable routing protocol of Optimized Link State Routing (OLSR) algorithm to looking for a delivery route of data packets to the end node. The OLSR routing protocol uses MPRs node as selected nodes, which forwards the control messages of networks topology information. This could streamline the network traffic of DTN to determine the best route of delivery data packets from the sender to the receiver. This paper presents transmission of medical data from the source of the node in the rural area to the destination node through the nodes of intermediate. This telemedicine system sends medical data of patient's private data that is encrypted using Advanced Encryption System (AES) to guarantee the medical data safety. The AES encryption system takes no more than 0.4 ms for data encrypting and decrypting process.

With the global population aging and the increasing demand for health, fueled by the development and application of the Internet of Things, smart healthcare has become a trend in the medical industry, further promoting medical intelligence and data integration. This paper focuses on the application of embedded systems in the smart healthcare Internet of Things (IoT), exploring the current state, key technologies, challenges, and development directions through literature review and case analysis. Research has demonstrated that embedded systems significantly enhance healthcare efficiency and quality, optimise resource allocation, and foster healthcare innovation. Additionally, this paper suggests that in the future of smart healthcare, there is a need to strengthen technology research and development and explore new application models to accelerate smart healthcare growth. It also advocates promoting the unification of standards in the medical system nationwide. Furthermore, the paper emphasizes deepening the integration of AI with medical systems to enhance the security of patient medical data.

Medical data security is an important guarantee for intelligent medical system. Medical video data can help doctors understand the patients’ condition. Medical video retargeting can greatly reduce the storage capacity of data on the premise of preserving the original content information as much as possible. The smaller volume of medical data can reduce the execution time of data encryption and threat detection algorithm and improve the performance of medical data security methods. The existing methods mainly focus on the temporal pixel relationship and foreground motion between adjacent frames, but these methods ignore the user’s attention to the video content and the impact of background movement on retargeting, resulting in serious deformation of important content and area. To solve the above problems, this paper proposes an innovative video retargeting method, which is based on visual attention and motion estimation. Firstly, the visual attention map is obtained from eye tracking data, by K-means clustering method and Euclidean distance factor equation. Secondly, the motion estimation map is generated from both the foreground and background displacements, which are calculated based on the feature points and salient object positions between adjacent frames. Then, the visual attention map, the motion estimation map, and gradient map are fused to the importance map. Finally, video retargeting is performed by mesh deformation based on the importance map. Experiment on open datasets shows that the proposed method can protect important area and has a better effect on salient object flutter suppression.

Wearable devices for biomedical instrumentation could generate the medical data and transmit to a repository on cloud service through wireless networks. In this process, the private medical data will be disclosed by man in the middle attack. Thus, the archived data for healthcare services would be protected by non-standardized security policy by healthcare service provider (HSP) because HIPAA only defines the security rules. In this paper, we adopted the Advanced Encryption Standard (AES) for security framework on wearable devices, so healthcare applications using this framework could support the confidentiality easily. The framework developed as dynamic loadable module targeted for lightweight microcontroller such as msp430 within embedded operating system. The performance was shown that the module can support the real-time encryption using electrocardiogram and photoplethysmogram. In this regard, the processing load for enabling security is distributed to wearable devices, and the customized data protection method could be composed by HSP for a trusted healthcare service.

Venous thromboembolism (VTE) is a common vascular disorder requiring extended anticoagulation therapy postdischarge to reduce recurrence risk. Home rehabilitation management systems that use electronic health records from hospital care provide opportunities for continuous patient monitoring. However, transferring medical data from clinical to home settings raises significant concerns about privacy and security. Conventional methods such as manual data entry, optical character recognition, and dedicated data transmission lines face notable technical and operational challenges. This study aims to develop a QR code-based security transmission algorithm using Avro and byte pair encoding (BPE). The algorithm supports the secure creation and transfer of out-of-hospital health records by enabling patients to scan QR codes via a dedicated mobile app, ensuring data security and user privacy. Between January and October 2024, 300 hospitalized patients with VTE were recruited at the Sixth Medical Center of the Chinese PLA General Hospital. Post discharge, participants used a home rehabilitation app tailored for VTE management. The QR code-based security transmission algorithm was developed to securely transfer in-hospital electronic health records to the out-of-hospital app. It uses BPE, Avro, and Gzip for optimized data compression and uses ChaCha20 and BLAKE3 for encryption and authentication. Specifically, BPE tokenizes medical text, while Avro serializes JSON (JavaScript Object Notation) objects, contributing to data encryption. A proprietary tokenizer was trained, and compression efficiency was evaluated using a "Performance Benchmark Dataset." Comparative analyses were conducted to assess the compression efficiency of JSON serialization methods (Avro and ASN.1 [Abstract Syntax Notation One]), and tokenization algorithms (BPE and unigram). The dataset consisted of JSON files from 300 patients, averaging 240.1 fields per file (range 89-623) and 7095 bytes in size (range 2748-17,425 bytes). Using the BPE + Avro + Gzip algorithm, the average file size was reduced to 1048 bytes, achieving a compression ratio of 6.67. This was 1.82 times more efficient than traditional Gzip compression (average file size: 1907 bytes; compression ratio: 3.66; P<.001). For Chinese medical text tokenization, BPE outperformed unigram with a compression ratio of 4.68 versus 4.55 (P<.001). Avro and ASN.1 demonstrated comparable compression ratios of 2.57 and 2.59, respectively, when used alone (P=.30). However, Avro combined with BPE and Gzip significantly outperformed ASN.1, achieving compression ratios of 6.67 versus 5.21 (P<.001). Additionally, 84.7% (254/300) of patients needed to scan only 1 QR code, requiring an average of 3.1 seconds. The QR code-based security transmission algorithm using Avro and BPE efficiently compresses and transmits data in an encrypted manner and authenticates the identity of the scanning users, ensuring the privacy and security of medical data. Delivered as a software development kit, the algorithm offers straightforward implementation and usability, supporting its broad adoption across various applications.

Lung cancer continues to be one of the most widespread and deadly cancer diagnoses that affects humans worldwide. Early detection of lung cancer leads to decreased mortality rates; however, several challenges hinder the development and deployment of effective predictive models. These challenges consist of mainly the problem of high computational power to evaluate large-scale medical data, privacy and security of medical data, limited data sharing between medical organizations and interpretability to handle the black box problem that AI-based models face. Such limitations have posed severe difficulties to the utilization of conventional approaches in the prediction of lung cancer, thus limiting them most importantly for general use, especially in clinical practice settings in real time. To address these challenges, this research introduced a novel lung cancer prediction model that utilizes an integrated framework combining MapReduce, Private Blockchain, Federated Learning (FL), and Explainable Artificial Intelligence (XAI). It improves lung cancer detection using MapReduce to handle large lung cancer datasets, supporting rapid and scalable learning. Private Blockchain is used for the secure, tamper-proof, and immutable processing of patient information, whereas FL allows healthcare sectors to train models together, without compromising patients’ privacy. Moreover, it also employed XAI to improve the model’s interpretability so clinicians can understand and rely on AI predictions. Together, these methods improve AI’s efficiency and trustworthiness in medical applications. This proposed model provides better and more secure lung cancer predictions, ensuring interpretability and collaboration. With an exceptional accuracy of 98.21% and a miss rate of just 1.79%, it outperforms previously published approaches, establishing a new benchmark for privacy-preserving, explainable, and scalable AI models in healthcare.

The current Internet infrastructure, initially conceived to provide closed group connectivity to a limited user-base, is now facing the challenges of catering to over three billion users with dynamically changing data, transport, and access requirements. Content-Centric Networking (CCN) is being explored as a possible future Internet architecture. Named-Data Networking (NDN) exemplifies CCN and with the ever-changing security landscape, it is imperative to build it with intrinsic resilience based on the security by design narrative. In this extended abstract, we explore the fake interest fiooding by adversaries to launch a Distributed Denial of Service (DDoS) attack in NDN. We propose Kiram as an intelligent anomaly recognition and alert generation mechanism serving as a collaborative countermeasure to a DDoS attack. The alert generated by Kiram is named Warding Off Evil (WOE). This poster presents results establishing the effectiveness of Kiram based on substantial simulations over a fairly realistic topology.

BackgroundPrivacy is of increasing interest in the present big data era, particularly the privacy of medical data. Specifically, differential privacy has emerged as the standard method for preservation of privacy during data analysis and publishing.ObjectiveUsing machine learning techniques, we applied differential privacy to medical data with diverse parameters and checked the feasibility of our algorithms with synthetic data as well as the balance between data privacy and utility.MethodsAll data were normalized to a range between –1 and 1, and the bounded Laplacian method was applied to prevent the generation of out-of-bound values after applying the differential privacy algorithm. To preserve the cardinality of the categorical variables, we performed postprocessing via discretization. The algorithm was evaluated using both synthetic and real-world data (from the eICU Collaborative Research Database). We evaluated the difference between the original data and the perturbated data using misclassification rates and the mean squared error for categorical data and continuous data, respectively. Further, we compared the performance of classification models that predict in-hospital mortality using real-world data.ResultsThe misclassification rate of categorical variables ranged between 0.49 and 0.85 when the value of ε was 0.1, and it converged to 0 as ε increased. When ε was between 102 and 103, the misclassification rate rapidly dropped to 0. Similarly, the mean squared error of the continuous variables decreased as ε increased. The performance of the model developed from perturbed data converged to that of the model developed from original data as ε increased. In particular, the accuracy of a random forest model developed from the original data was 0.801, and this value ranged from 0.757 to 0.81 when ε was 10-1 and 104, respectively.ConclusionsWe applied local differential privacy to medical domain data, which are diverse and high dimensional. Higher noise may offer enhanced privacy, but it simultaneously hinders utility. We should choose an appropriate degree of noise for data perturbation to balance privacy and utility depending on specific situations.

Recent technologies such as Big Data could be used in order to help the improvement of other fields except for the telecommunication field. Due to the Health field, the Big Data technology could contribute with the aim to help for the purpose of analysis and management of the huge amounts of health data. The main objective of this research proposal is an analytical study of the technologies IoT, Cloud Computing (CC) and large-scale data (Big Data) to resolve various issues facing the health sector in relation to these technologies. The purpose of the research proposal is the collection of medical (e-health) big data in real time. The collection will be performed by sensor devices and actuators, which will wear patients who suffer from various ailments. Then is following, the transfer of these data through a network to a cloud server. Additionally, these data will be processed in the cloud which makes its analysis so as to become meaningless. By the analysis of these data is done the data mining. Finally, to address the various problems in the health sector, the transfer of the analyzed health data will be held by the devices of the relevant persons. Also, in our study, we will deal with the security of medical data which constitute personal data and must be protected.

The primary objective of this chapter is to analyze the existing tools and techniques for medical data security. Typically, medical data includes either medical signals such as electrocardiogram, electroencephalogram, electromyography, or medical imaging like digital imaging and communications in medicine, joint photographic experts group format. The medical data are sensitive, subject to privacy preservation, and data access rights. Security in e-health field is an integrated concept which includes robust combination of confidentiality, integrity, and availability of medical data. Confidentiality ensures the data is inaccessible to unauthorized access. Integrity restricts the alteration in data by the unauthorized user. Whereas availability provides the readiness of the data when needed by the authorized user. Additionally, confidentiality, integrity and availability, accountability parameter records the back action list which answers the why, when, what, and whom data is accessed. The selected tools and techniques used in medical data security in e-health applications is discussed.