Our method is based on a scalable outlier detection technique that uses attribute value frequencies. We provide an end-to-end privacy guarantee by using the ...
Our method is based on a scalable outlier detection technique that uses attribute value frequencies. We provide an end-to-end privacy guarantee by using the ...
Abstract—Collaborative analytics is crucial to extract value from data collected by different organizations and stored in separate silos.
Differentially Private Outlier Detection in a Collaborative Environment
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We then develop a novel method to find outliers from both horizontally partitioned and vertically partitioned categorical data in a privacy-preserving manner.
Our method is based on a scalable outlier detection technique that uses attribute value frequencies. We provide an end-to-end privacy guarantee by using the ...
Jul 9, 2020 · There are no outlier detection methods for categorical data. The notion means nothing in this case. You might think like that:.
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We then develop a novel method to find outliers from both horizontally partitioned and vertically partitioned categorical data in a privacy-preserving manner.
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We then develop a novel method to find outliers from both horizontally partitioned and vertically partitioned categorical data in a privacy-preserving manner.
When the database is partitioned between multiple parties, which collaborate ... For outlier detection on categorical data, distance-based approaches do not make ...
Sep 1, 2018 · Our method is based on a scalable outlier detection technique that uses attribute value frequencies. We provide an end-to-end privacy guarantee ...