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Privacy-preserving imputation of missing data (2008)
| Content Provider | CiteSeerX |
|---|---|
| Author | Jagannathan, Geetha Wright, Rebecca N. |
| Abstract | Handling missing data is a critical step to ensuring good results in data mining. Like most data mining algorithms, existing privacy-preserving data mining algo-rithms assume data is complete. In order to maintain privacy in the data mining process while cleaning data, privacy-preserving methods of data cleaning will be required. In this paper, we address the problem of privacy-preserving data imputa-tion of missing data. Specifically, we present a privacy-preserving protocol for filling in missing values using a lazy decision tree imputation algorithm for data that is horizontally partitioned between two parties. The participants of the protocol learn only the imputed values; the computed decision tree is not learned by either party. |
| File Format | |
| Journal | Data Knowl. Eng |
| Language | English |
| Publisher Date | 2008-01-01 |
| Access Restriction | Open |
| Subject Keyword | Privacy-preserving Imputation Imputed Value Data Mining Algorithm Privacy-preserving Method Algo-rithms Assume Data Good Result Computed Decision Tree Lazy Decision Tree Imputation Algorithm Data Cleaning Privacy-preserving Data Imputa-tion Privacy-preserving Data Critical Step Data Mining Data Mining Process Privacy-preserving Protocol |
| Content Type | Text |
| Resource Type | Article |
