The advent of miniature biosensors has generated numerous opportunities for deploying wireless sensor networks in healthcare. However, an important barrier is that acceptance by healthcare stakeholders is influenced by the effectiveness of privacy safeguards for personal and intimate information which is collected and transmitted over the air, within and beyond these networks. In particular, these networks are progressing beyond traditional sensors, towards also using multimedia sensors, which raise further privacy concerns. Paradoxically, less research has addressed privacy protection, compared to security. Nevertheless, privacy protection has gradually evolved from being assumed an implicit by-product of security measures, and it is maturing into a research concern in its own right. However, further technical and socio-technical advances are needed. As a contribution towards galvanising further research, the hallmarks of this paper include: (i) a literature survey explicitly anchored on privacy preservation, it is underpinned by untangling privacy goals from security goals, to avoid mixing privacy and security concerns, as is often the case in other papers; (ii) a critical survey of privacy preservation services for wireless sensor networks in healthcare, including threat analysis and assessment methodologies; it also offers classification trees for the multifaceted challenge of privacy protection in healthcare, and for privacy threats, attacks and countermeasures; (iii) a discussion of technical advances complemented by reflection over the implications of regulatory frameworks; (iv) a discussion of open research challenges, leading onto offers of directions for future research towards unlocking the door onto privacy protection which is appropriate for healthcare in the twenty-first century.
Enhancing Data Privacy In Wireless Sensor Networks: Investigating Techniques And Protocols To Protect Privacy Of Data Transmitted Over Wireless Sensor Networks In Critical Applications Of Healthcare And National Security
University of Quebec（魁北克大学）；ETS（高等技术学院）；Princeton University（普林斯顿大学）
Recent advances in electronic devices and communication infrastructure have revolutionized the traditional healthcare system into a smart healthcare system by using IoMT devices. However, due to the centralized training approach of artificial intelligence (AI), the use of mobile and wearable IoMT devices raises privacy concerns with respect to the information that has been communicated between hospitals and end users. The information conveyed by the IoMT devices is highly confidential and can be exposed to adversaries. In this regard, federated learning (FL), a distributive AI paradigm has ope…
Healthcare data in cloud computing facilitates the treatment of patients efficiently by sharing information about personal health data between the healthcare providers for medical consultation. Furthermore, retaining the confidentiality of data and patients' identity is a another challenging task. This paper presents the concept of an access control-based (AC) privacy preservation model for the mutual authentication of users and data owners in the proposed digital system. The proposed model offers a high-security guarantee and high efficiency. The proposed digital system consists of four diffe…
Privacy preservation is an important issue in today's context of extreme penetration of internet and mobile technologies. It is more important in the case of Wireless Sensor Networks (WSNs) where collected data often requires in-network processing and collaborative computing. Researches in this area are mostly concentrated in applying data mining techniques to preserve the privacy content of the data. These techniques are mostly computationally expensive and not suitable for resource limited WSN nodes. In this paper, a scheme is developed to provide privacy preservation in a much simpler way w…
With the proliferation of wireless networks, mobile devices and medical devices are increasingly being equipped with wireless interfaces, such as Bluetooth and WiFi to allow easy access to and control of the medical devices. Unfortunately, the very presence and usage of such interfaces also expose the medical devices to novel attacks from malicious parties. The emerging threat from malicious mobile devices is significant and severe, since attackers can steal confidential data from a patient's medical device. Also, attackers can compromise the medical device and either feed doctors bad data fro…
Healthcare applications are well thought-out as interesting fields for WSN where patients can be examine using wireless medical sensor networks. Inside the hospital or extensive care surroundings there is a tempting need for steady monitoring of essential body functions and support for patient mobility. Recent research cantered on patient reliable communication, mobility, and energy-efficient routing. Yet deploying new expertise in healthcare applications presents some understandable security concerns which are the important concern in the inclusive deployment of wireless patient monitoring sy…
Sensitive records stored in the cloud such as healthcare records, private conversation and credit card information are targets of hackers and privacy abuse. Current information and record management systems have difficulties achieving privacy protection of such sensitive records in a secure, transparent, decentralized and trustless environment. The Blockchain technology is a nascent and a promising technology that facilitates data sharing and access in a secure, decentralized and trustless environment. The technology enables the use of smart contracts that can be leveraged to complement existi…
In ubiquitous computing domain context awareness is an important issue. So, in ubiquitous computing, mere protection of message confidentiality is not sufficient for most of the applications where context-awareness can lead to near deterministic ideas. An adversary might deduce sensitive information by observing the contextual data, which when correlated with prior information about the people and the physical locations that are being monitored by a set of sensors can reveal most of the sensitive information. So, it is obvious that for security and privacy preservation in ubiquitous computing …
A few years ago, wireless sensor networks (WSNs) used by only military. Now, we have seen many of organizations use WSNs for some purposes such as weather, pollution, traffic control, and healthcare. Security is becoming on these days a major concern for wireless sensor network. In this paper I focus on the security types of attacks and their detection. This paper anatomizes the security requirements and security attacks in wireless sensor networks. Also, indicate to the benchmarks for the security in WSNs.
Charles Sturt University (CSU)（查尔斯特大学）；University of Western Sydney (UWS)（西悉尼大学）；Kent Institute Australia（澳大利亚肯特学院）；Asia Pacific International College (APIC)（亚太国际学院）；Australian Institute of Higher Education（澳大利亚高等教育学院）；AL Nisour University College（阿尔尼苏尔大学学院）；University of Kurdistan Hewler（库尔德斯坦赫勒大学）；AMA International University Bahrain (AMAIUB)（巴林AMA国际大学）
Health records data security is one of the main challenges in e-health systems. Authentication is one of the essential security services to support the stored data confidentiality, integrity, and availability. This research focuses on the secure storage of patient and medical records in the healthcare sector where data security and unauthorized access is an ongoing issue. A potential solution comes from biometrics, although their use may be time-consuming and can slow down data retrieval. This research aims to overcome these challenges and enhance data access control in the healthcare sector t…
Indian Institute of Technology (Banaras Hindu University) Varanasi（瓦拉纳西印度理工学院（贝拿勒斯印度教大学））
This paper proposes Federated Learning (FL) based smart healthcare system where Medical Centers (MCs) train the local model using the data collected from patients and send the model weights to the miners in a blockchain-based robust framework without sharing raw data, keeping privacy preservation into deliberation. We formulate an optimization problem by maximizing the utility and minimizing the loss function considering energy consumption and FL process delay of MCs for learning effective models on distributed healthcare data underlying a blockchain-based framework. We propose a solution in t…
The advancement in the healthcare sector is entering into a new era in the form of Health 4.0. The integration of innovative technologies like Cyber-Physical Systems (CPS), Big Data, Cloud Computing, Machine Learning, and Blockchain with Healthcare services has led to improved performance and efficiency through data-based learning and interconnection of systems. On the other hand, it has also increased complexities and has brought its own share of vulnerabilities due to the heavy influx, sharing, and storage of healthcare data. The protection of the same from cyber-attacks along with privacy p…
TinyML NLP Scheme for Semantic Wireless Sentiment Classification with Privacy Preservation
Department of Electrical Engineering and Computer Science, York University（电气工程与计算机科学系，约克大学）；CEMSE Division, King Abdullah University of Science and Technology (KAUST)（CEMSE部门，国王 Abdullah 科学与技术大学（KAUST））
Hierarchical Dual-Strategy Unlearning for Biomedical and Healthcare Intelligence Using Imperfect and Privacy-Sensitive Medical Data
Department of Neurosurgery, Qilu Hospital of Shandong University and Institute of Brain and Brain-Inspired Science（齐鲁医院山东大学神经外科和脑科学与脑启发科学研究院）；Department of Neurosurgery, The Affiliated Hospital of Qingdao University（青岛大学附属医院神经外科）；Department of Neurosurgery, Peking Union Medical College Hospital（北京地坛医院神经外科）；School of Software and Microelectronics, Peking University（北京大学软件与微电子学院）；College of Artificial Intelligence, Dalian Maritime University（大连海事大学人工智能学院）；University of Colorado Boulder（科罗拉多大学波尔德分校）；Zhengzhou University（郑州大学）；The University of Hong Kong（香港大学）；Georgia Institute of Technology（佐治亚理工学院）
Wireless body area network(WBAN) is becoming more popular in recent years. Security and privacy of this network is the major concern for the researchers. Most of the WBAN sensors currently available in the market uses Bluetooth Low Energy (BLE) protocol for connection. But the BLE protocol has an inherent security flaw due to its weak pairing protocol which can be easily be exploited by an attacker. In this paper, a different pairing protocol based on Lightweight Anonymous Authentication Protocol (LWAA) is proposed. Proposed protocol has a 3.8% power overhead over traditional BLE. But this is …
Recent emergence of electronic culture uplifts healthcare facilities to a new era with the aid of wireless sensor network (WSN) technology. Due to the sensitiveness of medical data, austere privacy and security are inevitable for all parts of healthcare systems. However, the constantly evolving nature and constrained resources of sensors in WSN inflict unavailability of a lucid line of defense to ensure perfect security. In order to provide holistic security, protections must be incorporated in every component of healthcare sensor networks. This paper proposes an efficient security scheme for …
The recent advancements in computing systems and wireless communications have made healthcare systems more efficient than before. Modern healthcare devices can monitor and manage different health conditions of the patients automatically without any manual intervention from medical professionals. Additionally, the use of implantable medical devices (IMDs), body area networks (BANs), and Internet of Things (IoT) technologies in healthcare systems improve the overall patient monitoring and treatment process. However, these systems are complex in software and hardware, and optimizing between secur…
Process mining techniques help to improve processes using event data. Such data are widely available in information systems. However, they often contain highly sensitive information. For example, healthcare information systems record event data that can be utilized by process mining techniques to improve the treatment process, reduce patient's waiting times, improve resource productivity, etc. However, the recorded event data include highly sensitive information related to treatment activities. Responsible process mining should provide insights about the underlying processes, yet, at the same …
Wearable devices enable users to collect health data and share them with healthcare providers for improved health service. Since health data contain privacy-sensitive information, unprotected data release system may result in privacy leakage problem. Most of the existing work use differential privacy for private data release. However, they have limitations in healthcare scenarios because they do not consider the unique features of health data being collected from wearables, such as continuous real-time collection and pattern preservation. In this paper, we propose Re-DPoctor, a real-time healt…
Privacy-Preserving Data Aggregation Techniques for Enhanced Efficiency and Security in Wireless Sensor Networks: A Comprehensive Analysis and Evaluation
This book chapter identifies various security threats in wireless mesh network (WMN). Keeping in mind the critical requirement of security and user privacy in WMNs, this chapter provides a comprehensive overview of various possible attacks on different layers of the communication protocol stack for WMNs and their corresponding defense mechanisms. First, it identifies the security vulnerabilities in the physical, link, network, transport, application layers. Furthermore, various possible attacks on the key management protocols, user authentication and access control protocols, and user privacy …
Since the number of elderly and patients who are in hospitals and healthcare centers are growing, providing efficient remote healthcare services seems very important. Currently, most such systems benefit from the distribution and autonomy features of multiagent systems and the structure of wireless sensor networks. On the one hand, securing the data of remote healthcare systems is one of the most significant concerns; particularly recent types of research about the security of remote healthcare systems keep them secure from eavesdropping and data modification. On the other hand, existing remot…
Analytics for Cyber Defense (ACyD) Lab（网络防御分析实验室）；Cyber-Physical Systems Security Lab（网络物理系统安全实验室）；Florida International University（佛罗里达国际大学）
Smart healthcare systems (SHSs) are providing fast and efficient disease treatment leveraging wireless body sensor networks (WBSNs) and implantable medical devices (IMDs)-based internet of medical things (IoMT). In addition, IoMT-based SHSs are enabling automated medication, allowing communication among myriad healthcare sensor devices. However, adversaries can launch various attacks on the communication network and the hardware/firmware to introduce false data or cause data unavailability to the automatic medication system endangering the patient's life. In this paper, we propose SHChecker, a…
The landscape of miniaturized wireless biomedical devices (MWBDs) is rapidly expanding as proactive mobile healthcare proliferates. MWBDs are diverse and include various injectable, ingestible, implantable, and wearable devices. While the growth of MWBDs increases the flexibility of medical services, the adoption of these technologies brings privacy and security risks for their users. MWBDs can operate with sensitive, private information and affect patients through the use of stimulation and drug delivery. Therefore, these devices require trust and need to be secure. Embedding protective mecha…
Privacy Challenges and Solutions in Retrieval-Augmented Generation-Enhanced LLMs for Healthcare Chatbots: A Review of Applications, Risks, and Future Directions
Centre for Smart Health, School of Nursing（智能健康中心、护理学院）；The Hong Kong Polytechnic University（香港理工大学）；Department of Electrical and Electronic Engineering（电子与电气工程系）；Faculty of Health Sciences（健康科学学院）；University of Maribor（马里博大学）
Smart Medical IoT Security Vulnerabilities: Real-Time MITM Attack Analysis, Lightweight Encryption Implementation, and Practitioner Perceptions in Underdeveloped Nigerian Healthcare Systems
La Trobe University（拉筹伯大学）；CSIRO’s Data61（联邦科学与工业研究组织数据61所）；Cybersecurity CRC（网络安全合作研究中心）；Nanjing University of Science and Technology（南京理工大学）
Split learning (SL) enables data privacy preservation by allowing clients to collaboratively train a deep learning model with the server without sharing raw data. However, SL still has limitations such as potential data privacy leakage and high computation at clients. In this study, we propose to binarize the SL local layers for faster computation (up to 17.5 times less forward-propagation time in both training and inference phases on mobile devices) and reduced memory usage (up to 32 times less memory and bandwidth requirements). More importantly, the binarized SL (B-SL) model can reduce priv…
Electronic medical record (EMR) is a crucial form of healthcare data, currently drawing a lot of attention. Sharing health data is considered to be a critical approach to improve the quality of healthcare service and reduce medical costs. However, EMRs are fragmented across decentralized hospitals, which hinders data sharing and puts patients' privacy at risks. To address these issues, we propose a blockchain based privacy-preserving data sharing for EMRs, called BPDS. In BPDS, the original EMRs are stored securely in the cloud and the indexes are reserved in a tamper-proof consortium blockcha…
Privacy problems are lethal and getting more attention than any other issue with the notion of the Internet of Things (IoT). Since IoT has many application areas including smart home, smart grids, smart healthcare system, smart and intelligent transportation and many more. Most of these applications are fueled by the resource-constrained sensor network, such as Smart healthcare system is powered by Wireless Body Area Network (WBAN) and Smart home and weather monitoring systems are fueled by Wireless Sensor Networks (WSN). In the mentioned application areas sensor node life is a very important …
University of Technology Sydney（悉尼科技大学）；Data61, CSIRO（澳大利亚联邦科学与工业研究组织 Data61）；The University of Sydney（悉尼大学）；La Trobe University（乐卓博大学）；The University of Melbourne（墨尔本大学）
The newly emerged machine learning (e.g. deep learning) methods have become a strong driving force to revolutionize a wide range of industries, such as smart healthcare, financial technology, and surveillance systems. Meanwhile, privacy has emerged as a big concern in this machine learning-based artificial intelligence era. It is important to note that the problem of privacy preservation in the context of machine learning is quite different from that in traditional data privacy protection, as machine learning can act as both friend and foe. Currently, the work on the preservation of privacy an…
