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Computing Covariance and Correlation in Optimally Privacy-Protected Statistical Databases: Feasible Algorithms
[chapter]
2014
Studies in Fuzziness and Soft Computing
In many real-life situations, e.g., in medicine, it is necessary to process data while preserving the patients' confidentiality. One of the most efficient methods of preserving privacy is to replace the exact values with intervals that contain these values. For example, instead of an exact age, a privacy-protected database only contains the information that the age is, e.g., between 10 and 20, or between 20 and 30, etc. Based on this data, it is important to compute correlation and covariance
doi:10.1007/978-3-319-03674-8_35
fatcat:nwc5zyy5ivbnxpc56lexuqf2ze