Healthcare System with Intrusion Detection and Privacy Protection based Cloudlet

2018 ◽  
Vol 6 (9) ◽  
pp. 5-8
Author(s):  
Kavita V. Dubey ◽  
Vina M. Lomte
2020 ◽  
Author(s):  
Adam Sadilek ◽  
Luyang Liu ◽  
Dung Nguyen ◽  
Methun Kamruzzaman ◽  
Benjamin Rader ◽  
...  

AbstractPrivacy protection is paramount in conducting health research. However, studies often rely on data stored in a centralized repository, where analysis is done with full access to the sensitive underlying content. Recent advances in federated learning enable building complex machine-learned models that are trained in a distributed fashion. These techniques facilitate the calculation of research study endpoints such that private data never leaves a given device or healthcare system. We show on a diverse set of health studies that federated models can achieve the same level of accuracy, precision, and generalizability, and result in the same interpretation as standard centralized statistical models whilst achieving significantly stronger privacy protections. This work is the first to apply modern and general federated learning methods to clinical and epidemiological research -- across a spectrum of units of federation and model architectures. As a result, it enables health research participants to remain in control of their data and still contribute to advancing science -- aspects that used to be at odds with each other.


2018 ◽  
Vol 7 (2.32) ◽  
pp. 164
Author(s):  
Syed. Karimunnisa ◽  
K Suma Anusha

With the development of clouds and cloudlet technology along with wearable devices, the need for providing security to medical data can be increased. Medical data includes data collection, data storage and data sharing, etc. Traditional healthcare system transmits the medical data to the cloud using sensitive information which causes communication energy consumption. Practically, sharing medical data is a challenging task. Thus in this paper, we propose a novel healthcare system by using the flexibility of cloudlet. The operations of cloudlet include privacy protection, data sharing and intrusion detection. In data collection stage, First, the data collected by wearable devices is encrypted using Number Theory Research Unit (NTRU) method and that encrypted data can be transfered to nearby cloudlet. Secondly, we develop a new trust model to help users to select trustable similar patients who want to share stored data in the cloudlet and to communicate with each other about their diseases. Thirdly, we divide users’ medical data into three parts and give them security which is stored in remote cloud of hospital. Finally, to protect the healthcare system from malicious attacks, we implement a novel collaborative intrusion detection system (IDS) method based on cloudlet mesh, Our experiments proves the effectiveness of the proposed scheme.  


2014 ◽  
Vol 543-547 ◽  
pp. 3646-3649
Author(s):  
Dong Sheng Zhang

To resolve conflicts between share and collaborative analysis requirements of security alarm and alert data holders worries about privacy, it firstly probes into the anonymized protection method Incognito. Based on that, it improves the algorithm to solve existing problems by extending common data like privacy protection targets to alert data. The generalized anonymous processing model for alert data is developed and the quantitative evaluation is realized between the level of alert datas secret protection and data quality. With authoritative data set of intrusion detection attack scenario as test data, the experiment validates efficiency and effectiveness of the proposed method on the part of performance and security.


2010 ◽  
Vol 43 (13) ◽  
pp. 77
Author(s):  
MARY ELLEN SCHNEIDER
Keyword(s):  

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