NDLI: GeCo: an online personal data generator and corruptor

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GeCo: an online personal data generator and corruptor

Content Provider	ACM Digital Library
Author	Vatsalan, Dinusha Tran, Khoi-Nguyen Christen, Peter
Abstract	We demonstrate GeCo, an online personal data GEnerator and COrruptor that facilitates the creation of realistic personal data ranging from names, addresses, and dates, to social security and credit card numbers, as well as numerical values such as salary or blood pressure. Using an intuitive Web interface, a user can create records containing such data according to their needs, and apply various corruption functions to generate duplicates of these records. Synthetic personal data are increasingly required in areas such as record de-duplication, fraud detection, cloud computing, and health informatics, where data quality issues can significantly affect the outcomes of data integration, processing, and mining projects. Privacy concerns, however, often make it difficult for researchers to obtain real data that contain personal details. Compared to other data generators that have to be downloaded, installed and customized,GeCo allows the creation of personal data with much less effort. In this demonstration we show (1) how different types of attributes, and dependencies between them, can be specified; (2) how the generated data can be modified using various types of corruption functions; and (3) how a user can contribute to GeCo by providing attribute generation functions and look-up files. We believe GeCo will be a valuable tool for researchers that require realistic personal data to evaluate their algorithms with regard to efficiency and effectiveness.
Starting Page	2473
Ending Page	2476
Page Count	4
File Format	PDF
ISBN	9781450322638
DOI	10.1145/2505515.2508207
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2013-10-27
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Data generation Duplicates Online demo Synthetic data
Content Type	Text
Resource Type	Article

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