European Journal of Molecular & Clinical Medicine

This article provides a complete review on new perspectives and systematic interpretations of published literatures by painstakingly organising them into subcategories. [This article] [gives] [a] comprehensive overview on new perspectives and this is accomplished by supplying a list of the various pieces of published literature. This article discusses the fundamental ideas behind the numerous existing data mining methods that protect users' privacy, as well as the benefits and drawbacks associated with these technologies. The techniques that are currently available for protecting the privacy of users during data mining can be categorised according to various aspects. These aspects include things like distortion, association rule, hide association rule, taxonomy, clustering, associative classification, outsourced data mining, distributed, and k-anonymity. The most salient advantages and disadvantages of the procedures are outlined here within their respective categories. This in-depth investigation highlights the historical development, the research issues that are occurring at the present time, the future tendencies, the gaps, and the deficiencies. It has been decided that obligatory additional significant changes must be implemented for the purpose of providing stronger protection and preservation of personal privacy

Better medical care is becoming more necessary as wearable gadgets and cloudlet technologies become in prominence. Data collection, storage, and dissemination, among other things, all fall within the purview of the medical data processing chain. In the traditional healthcare system, sensitive information about patients is often sent to the cloud, which consumes a great deal of energy and has a negative impact on the environment. In the real world, medical data exchange is a crucial and difficult subject. The versatility of the cloudlet is used to create a new healthcare system in this article. Cloudlet's features include data sharing, privacy protection, and intrusion detection. To encrypt user's body data collected by wearable devices, we use the Number Theory Research Unit (NTRU) method first. In order to save energy, this data will be sent to a neighbouring cloudlet in an efficient manner. To assist users identify trustworthy cloudlet partners, we provide a novel trust model that can be applied to the cloudlet. The trust model also facilitates communication between patients with similar illnesses. Third, we separate the medical data of patients stored in the hospital's remote cloud into three sections and safeguard them. At long last, we've developed a novel, collaborative intrusion detection system (IDS) method based on cloudlet mesh to safeguard the healthcare system's big data cloud remotely. The results of our experiments show that the proposed method is effective.

Maintaining privacy in data publishing is a major challenge. In complex world sensitive information privacy is the main issue. Many algorithms are used to protect sensitive information in mined data which is not efficient because resulted output can be easily linked with public data so it shows user identity. Many techniques are used to protect privacy in data mining. Anatomization approaches aim to avoid directly use of sensitive data. The growing popularity and development of anatomization technologies bring sensitive data and protect the security of sensitive information Anatomization. The anatomization approach dissociates the correlation observed between the quasi identifier attributes and sensitive attributes and yields two separate tables with non-overlapping attributes. In the slicing algorithm, vertical partitioning does the grouping of the correlated sensitive attributes in sensitive table together and thereby minimizes the dimensionality. Consequently, it becomes increasingly important to preserve the privacy of published data. An attacker is apt to identify an individual from the published tables, with attacks through the record linkage, attribute linkage, table linkage or probabilistic attack. Two comprehensive sets of real-world relationship data are applied to evaluate the performance of our anonymization approach. Simulations and privacy analysis show our scheme possesses better privacy while ensuring higher utility.

Thetechnology of engineering science is growingfaster and its usage isto bootincreasing speedily. The data and its usage has become an important issue in existence. There by the storage of data may be a crucial issue in means of life. For this data storage, Cloud Computing is useful. Cloud computing is that the follow of using a network of remote servers hosted on internet to store, manage and methodology data on demand and pay as per use. It provides access to a pool of shared resources instead of native servers or personal computers. as a result, outfit do not acquire the things physically, it saves managing value and time for organizations. As a result of the sector of cloud computing is spreading the new techniques are developing. This increase in cloud computing setting to boot can increase security challenges for cloud developers. Most of the organizations are an excellent deal concerned regarding the possession of their data.This analysis paper presents a review on the cloud computing concepts yet as security issues inherent at intervals the context of cloud computing and cloud infrastructure. This paper in addition analyses the key analysis and challenges that presents in cloud computing and offers best practices to service suppliers yet as enterprises. The foremost goal is to review different types of attacks and encryption techniques to secure the cloudmodel.