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Privacy Preserving Data Mining: An approach to safely share and use sensible medical dataGerhard Manner, Discovery Biomax Symposium, June 24th, 2016, Munich.discovery.privacy protection vs knowledge
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Start by understanding the purpose and goals of privacy preserving data mining.
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Identify the dataset that requires privacy preservation.
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Determine the specific privacy techniques or algorithms that should be applied.
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Clean and preprocess the data to ensure its quality and integrity.
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Implement the chosen privacy preserving techniques on the dataset.
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Evaluate the effectiveness and utility of the privacy preserving methods used.
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Who needs privacy preserving data mining?

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Government agencies that need to protect sensitive data while performing data mining for policy-making purposes.
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Healthcare organizations that want to analyze patient data without compromising privacy.
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Financial institutions that need to conduct data mining on customer information while ensuring data confidentiality.
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Research institutions that deal with sensitive data and want to preserve privacy during data analysis.
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E-commerce companies that want to analyze customer behavior without violating privacy regulations.
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Privacy preserving data mining is a technique used to extract useful information from data without compromising the privacy of individuals.
Any organization or individual who collects and analyzes data from individuals is required to use privacy preserving data mining techniques.
Privacy preserving data mining can be filled out by utilizing techniques such as anonymization, encryption, and data perturbation to protect sensitive information.
The purpose of privacy preserving data mining is to balance the need for data analysis with the need to protect the privacy of individuals.
Information such as the data collection methods used, the types of data analyzed, and the privacy protection measures implemented must be reported on privacy preserving data mining.
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