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How Privacy Preserving Data Mining Protects Your Information

665 words - 3 pages

The popularity of internet as a communication medium, there is an increasing need for distributing data that encompass personal information from large database. With the propagation of information about individual’s personal data available in the databases, data mining is considered as a threat to privacy of data. Extraction of concealed predictive information from large databases is a powerful technology with great potential to support companies focus on the most important information in their data warehouses. For Example in the Healthcare field, A hospital has collected useful information about a group of patient records that would help medical researchers and would like to publish this data while preserving the privacy of the individuals involved. To share data among hospitals and other providers use of health information beyond direct patient attention with privacy protection. Through data mining, attacker can able to extract confidential and useful information of individual which do not want to disclose to public.
The Privacy Preserving Data Mining(PPDM) is extensively used to protect such private information from any type of data mining algorithm. Several privacy preserving data mining methods have newly been developed. Secure Multi party computation technique which ensure robust privacy and reliability. But this approach suffers from poor performance. Mostly, the techniques for PPDM are based on cryptography methods, knowledge discovery and data hiding. In general, statistics-based and the crypto-based techniques are used to deal with Privacy preserving data mining. In the statistics-based technique, the data publisher’s sanitize the data through perturbation or generalization before publishing. The statistics-based approach is that it efficiently handles large volume of datasets. In the crypto-based approach, data owners have to cooperatively implement specially designed data mining algorithms. Though these algorithms achieve verifiable privacy protection but they suffers from performance and scalability issues.
Usage of hiding association rules is projected due to high performance which is generated from frequent item sets....

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