Abstract
In this modern era of thriving technology, the data being gathered through way of private in addition to public businesses is increasing each day. But in recent times people are more worried about their data and privateness being preserved at the same time as use of their data in other analysis purpose. Thus Privacy-Preserving Data Mining (PPDM) method has been proposed to permit the extraction of understanding from data at the same time as keeping the privateness of people. The primary purpose of our project is on preserving privacy for healthcare records as privateness lacks in Medical data. Privacy-Preserving Data Mining (PPDM) offers with shielding the privacy of individual’s data or sensitive data without the utility of data. Therefore the strategies like anonymization, randomization are used to attain the intention. However, unfortunately anonymization results in certain level of information loss while preserving privacy. In order to overcome this problem, perturbation technique is carried out. Our challenge initiates with cleaning and preprocessing followed by ensemble classification and proceeded with perturbation to attain the goal. This method focuses on preserving privacy by perturbing the sensitive attributes in the Medical data without causing loss to the information in the process
Highlights
Data mining is a technique employed to extract particular information from the available pool of statistics
A some of this data may be non-public and confidential and for this reason need to be protected. This information that's categorized as sensitive attribute must not be exposed because it consists of non-public data of individuals
Coronary heart disease dataset is selected for privacy preserving due to the fact cardiovascular disorder is a primary purpose of dying. an expected 17.5 million people died from cvd in 2012, representing 31% of all global death[7]
Summary
Data mining is a technique employed to extract particular information from the available pool of statistics. A some of this data may be non-public and confidential and for this reason need to be protected This information that's categorized as sensitive attribute must not be exposed because it consists of non-public data of individuals. For this reason Privacy Preserving Data Mining (PPDM) techniques are employed to guard this information. The attributes are age, intercourse, cp, trestbps, chol, fbs, restecg, thalach, exang, oldpeak, slope, ca, thal, and num The system includes 3 modules (i) In cleaning and preprocessing stage, the heart disease data set is in character form. Perturbation can be carried out by means of the usage of additive noise or multiplicative noise generation This technique is greater convenient for maintaining privacy
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