Posted by Admin: System Admin
In recent years, Internet of things (IoT)-enabled health monitoring wearable devices have become a trend in healthcare systems, regularly collecting vital sign data from patients and uploading them to the cloud. Through on-demand search queries, data are shared with third-party healthcare service providers (HSPs) to monitor patients' health status and provide timely diagnoses. To ensure privacy and security, patient health data should be encrypted before being uploaded to the cloud. The cloud can give search encryption services. However, current searchable encryption technologies still have problems with forward privacy security and verifiability. This paper proposes an IoT-cloud-enabled healthcare data system incorporating a searchable encryption method with forward privacy and verifiability. By designing a trapdoor permutation function, we render the resulting output indistinguishable from meaningless random data to the adversary. Thus, the adversary cannot judge the relationship between a newly inserted record and a past search token, and therefore, the system realizes forward privacy or forward secrecy. We propose a multi-keyword search verification mechanism based on a pseudo-random function (PRF). Our approach solves verifying the correctness of search results in the top-k search scenario with partial search results. A formal security analysis proves that our scheme achieves forward privacy preservation, which can help guarantee healthcare data privacy. Additionally, a performance evaluation shows that our method is efficient and effective, providing an information security system to preserve patient privacy in IoT-enabled healthcare systems.
The present article throws light on advancement in ICTs. It is an evident that highly intelligent and smart IoT based use cases are possible with the advent in ICTs like Internet of Things, 5G Cellular Technology and Cyber- Physical Systems (CPS). For an instance, people spend considerable amount of their earning towards health in the present scenario. In view of this, there is high- impact- on society use case in Healthcare as IoT enables Ambient Assisted Living (AAL), Mobile Health (mHealth) and Electronic Health (eHealth). The conventional healthcare services are prone to delay, wastage of time and money, besides causing death of people. With intelligence and prediction capabilities of IoT, Remote Patient Monitoring (RPM) on regular basis (home/office/in-hospital), for those who deliberately need it, can be exploited to overcome challenges thrown by conventional healthcare units. IoT based RPM with wearable devices, sensor network and other digital infrastructure form an early warning system for impending emergencies that lead to severe health issues and even death of patients is left untreated or even treatment is delayed. It is proposed that a secure and privacy preserving IoT integration with healthcare units for realizing a reliable, available and secure RPM system at the conclusion. An existing system provides secure RFID based authentication, end-to-end secure communications and privacy protection. The system includes MOTO 360 watch (biosensor | body sensor) with Android wearable OS, server with REST framework and a smart phone application to monitor and detect fall, blood pressure and heart rate. This motivating scenario is enriched with security and privacy. The empirical evaluation revealed that the proposed RPM has potential to help improve quality of life and healthcare services. Disadvantages ? The system is not implemented Machine Learning Algorithms to optimize datasets. ? The support vector machine (SVM) algorithm is a supervised classifier that is not applied widely to solve classification and regression problems.
? We propose a scheme called FEncKV, based on a trapdoor permutation and a status count. ? We prove that FEncKV has the feature of forward privacy, meaning that an adversary is unable to determine the relationship between a previous search query and a newly added record. Forward privacy and verifiability, or forward secrecy, has received recent attention in the field of searchable encryption (SE). The purpose of forward security is to prevent the server from judging whether the updated content contains keywords from previous user search requests. It also helps verifiability consider the malicious modification of search data on the server, which requires an additional authentication mechanism to ensure that users can judge whether search results are correct. However, few studies have been conducted on SE schemes that satisfy both forward security and verifiability. It remains necessary to design appropriate verification mechanisms to provide a defense for SE schemes' forward security properties against potential attack. Machine learning is an important component of the growing field of data science. Through the use of statistical methods, different type of algorithms is trained to make classifications or predictions, and to uncover key insights in this project. These insights subsequently drive decision making within applications and businesses, ideally impacting key growth metrics. Machine learning algorithms build a model based on this project data, known as training data, in order to make predictions or decisions without being explicitly programmed to do so. Machine learning algorithms are used in a wide variety of datasets, where it is difficult or unfeasible to develop conventional algorithms to perform the needed tasks. Advantages ? We improve on EncKV to satisfy the condition of forward privacy. This approach ensures that an adversary cannot learn the relationship between an inserted record and a previous search query. ? The main contribution of this system is used machine learning algoriths to predict the threats and to categories the threats