Advanced Healthcare Systems. Группа авторов
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Health and Wellbeing in the Community and Care at Home”. In this paper, they have proposed a generic model “PriGen” for securing patient health-related data with the help of cloud storage. PriGen facilitates access of patient health-related data without involvement of any other party from the cloud as well as hiding highly sensitive data. PriGen uses homomorphic function for encryption of data that needs very high security and which is critical. This algorithm preserves the highly sensitive health-related personal data in public cloud environment and maintains confidentiality of the patient data.
Bindahman et al. [19] proposed a paper in 2011 ICIEIS (Informatics Engineering and Information Science: International Conference) in which they talked about general concept of privacy problem related to patient health data. They discussed various available security measures and its performance comparison related to healthcare data. Based on that, they also suggested some techniques for those security-related problems.
Next is the paper written by Dubovitskaya et al. [20] entitled “ICT Systems Security and Privacy Protection: 30th IFIP TC 11 International Conference SEC 2015”. In this paper, they have discussed various problems in building of health-related database for heterogeneous environment, where data are coming from various sources of different network environment of different hospitals. The integration of data comes from different locations. They introduced scaling and securing techniques for patient e-health data. They have used an algorithm called RSDB (Representative Protein Sequence Database) for collecting patient data efficiently and securely that are coming from various sources.
A paper by Idoga et al. [22] has discussed different issues related to privacy in the application e-healthcare environment.
2.3 Architecture
In this section, we will discuss three-layer architecture of next-generation healthcare industry with smart sensors, fog node, and cloud computing. The new healthcare industry will change the way hospital staff or doctors treat the patient. This IoT-driven healthcare industry will get highly efficient environment at very low cost, which decreases the workload and increases the throughput. The layered architecture will do different tasks at different layer. The three layers are device layer, fog layer, and cloud layer as shown in Figure 2.2.
2.3.1 Device Layer
At this layer, a very large number of smart sensors are involved, which are gathering tons of health-related data in near real-time. Any patient or healthcare specialist can access these data using any web-enabled device like phone, tablet, or computers. What they require is a secure and stable communication protocol to next layer in this case (fog layer). There is various communication are available for the wireless sensor nodes for communication within each other or propagating information to next layer. But selection of best protocol from the pool of various protocols is a tedious task. There are some protocols which are widely adopted for some general data transfer tasks are Low-Energy Bluetooth and High-Fidelity Wi-Fi.
Figure 2.2 Three-layer architecture.
Low-Energy Bluetooth is good for beacon type signal and where power constrains matter too much or battery is irreplaceable (like in heart pacemaker). On the other hand, Wi-Fi is used where long-range and high-data rate required (like transferring raw ECG data).
2.3.2 Fog Layer
This layer consists of high-end processors, large and high-speed data storage, and Network Interface Card for communication over internet. The patient health data are very critical data a normal considerable amount of latency can cost lives, example of such type of data is Myocardial Infarction where latency of seconds can cause serious damage Singh et al. [26]. So, we cannot rely only on cloud for data processing and analyzing critical time sensitive data. In handling these types of data, we require analysis, processing, and storage as close as possible to the devices where data is generated Badidi et al. [23]. Thus, need of fog computing arises here, and it processes, analyzes, and stores (for further use) health-related data which is very time-sensitive in nature Kraemer et al. [28]. This layer also filters, compresses high volume raw data, and passes to next layer (cloud computing layer) for big data analytics purposes.
2.3.3 Cloud Layer
The cloud layer integrates data from various fog nodes and does analysis using deep learning, generates pattern, and gets future insights for the disease prediction. The cloud layer provides various connectivity protocols to address variety of users across the world Akintoye et al. [27]. The fog node across different geographical areas uses different communication channel like optical fibers, twisted pair, co-axial cable, satellite communication, and sometimes LTE. The cloud provides best data management techniques to health-related user for better management of large amount of patient data. The cloud healthcare system incorporates set of rules through which it can analyze patterns and can trigger alarm when any risky pattern detected. The cloud layer uptime should be as high as possible (very near to 100%). So, it can always be there for help when ever any request arises from persons involved in healthcare institutions.
2.4 Issues and Challenges
Integration of IoT and fog computing can take the healthcare industry to the next level. The smart health sensors of wearable IoT devices stays with the patient all the time and it monitors patient heart rate, blood pressure, body temperature, blood glucose, oxygen saturation, and much more in real time and passes these whole data to the fog node for the processing and storage and then to the cloud for deep learning Khan et al. [29]. All this data plays an important role in the healthcare industry. So, it should be protected from various threats and vulnerabilities. There are a number of security challenges that we should care about. Generally, the patient care industry invests very less amount of money on privacy and security of healthcare data. But in a smart healthcare system, security and privacy plays a very important role Hamid et al. [30]. The data produced by medical e-health sensors are very large and very sensitive. These data also contain patient private information. The patient data can be hampered on different stages like in data gathering from sensors and transfer it to fog nodes and sometimes on clouds also.
Some of the data risks that should be taken into account are integrity of data, authenticity of data, and auditing of data and private data of patients. Various mechanisms are shown in Table 2.1.
Integrity of Patient Data: It refers to availability of the same data in the whole system without change. Or you can say no modification in data throughout its life cycle. This means accuracy of data should not be tempered. There should be no unintentional change in data. Any intention should reflect immediately in the whole system. The main purpose of maintaining integrity is to ensure accuracy and reliability of the health data. Integrity can be sub-categorized in four categories: integrity defined by user, integrity of reference, integrity of various domains, and integrity of data entity.
Usability of Patient Information: Information usability refers to no unauthorized access of patient data generated or stored by smart healthcare systems. The use of deep learning on patient data can generate unique patterns and provide different solutions. The data generated by the system also comes at data privacy risks. The little bit of unwanted modification in data can cause serious issues.
Audition of Healthcare Data: Access of healthcare devices needs to be auditioned properly for monitoring of various mechanisms and techniques for identifying unwanted patterns. The integration of cloud computing resources may also create some trouble in security concerns because the cloud providers generally are usually unreliable in case of privacy terms. So, it requires a good audition procedure, the audition procedure consists of records used in operation, the service provider (in this case the cloud provider) and the user which is involved in patient care.
Privacy of Patient: