demonstration

GeCo: an online personal data generator and corruptor

Authors:

Khoi-Nguyen Tran,

Dinusha Vatsalan,

Peter ChristenAuthors Info & Claims

CIKM '13: Proceedings of the 22nd ACM international conference on Information & Knowledge Management

Pages 2473 - 2476

https://doi.org/10.1145/2505515.2508207

Published: 27 October 2013 Publication History

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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.

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Cited By

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Haddock BPletcher ABlair-Stahn NKeyes OKappel MBachmeier SLutze SAlbright JBowman AKinuthia CBurke-Conte ZMudambi RFlaxman A(2024)Simulated data for census-scale entity resolution research without privacy restrictions: a large-scale dataset generated by individual-based modelingGates Open Research10.12688/gatesopenres.15418.28(36)Online publication date: 18-Oct-2024
https://doi.org/10.12688/gatesopenres.15418.2
Haddock BPletcher ABlair-Stahn NKeyes OKappel MBachmeier SLutze SAlbright JBowman AKinuthia CBurke-Conte ZMudambi RFlaxman A(2024)Simulated data for census-scale entity resolution research without privacy restrictions: a large-scale dataset generated by individual-based modelingGates Open Research10.12688/gatesopenres.15418.18(36)Online publication date: 3-May-2024
https://doi.org/10.12688/gatesopenres.15418.1
Taylor IKaplan ABetancourt B(2024)Fast Bayesian Record Linkage for Streaming Data ContextsJournal of Computational and Graphical Statistics10.1080/10618600.2023.228357133:3(833-844)Online publication date: 3-Jan-2024
https://doi.org/10.1080/10618600.2023.2283571
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Index Terms

GeCo: an online personal data generator and corruptor
1. Information systems
  1. Data management systems
    1. Database management system engines
  2. Information systems applications
    1. Data mining

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Information & Contributors

Information

Published In

CIKM '13: Proceedings of the 22nd ACM international conference on Information & Knowledge Management

October 2013

2612 pages

ISBN:9781450322638

DOI:10.1145/2505515

General Chairs:
Qi He
LinkedIn, USA
,
Arun Iyengar
IBM T.J. Watson Research Center, USA
,
Program Chairs:
Wolfgang Nejdl
L3S Research Center, Germany
,
Jian Pei
Simon Fraser University, Canada
,
Rajeev Rastogi
Amazon, India

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 27 October 2013

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CIKM'13

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CIKM'13: 22nd ACM International Conference on Information and Knowledge Management

October 27 - November 1, 2013

California, San Francisco, USA

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CIKM '13 Paper Acceptance Rate 143 of 848 submissions, 17%;

Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

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Cited By

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Haddock BPletcher ABlair-Stahn NKeyes OKappel MBachmeier SLutze SAlbright JBowman AKinuthia CBurke-Conte ZMudambi RFlaxman A(2024)Simulated data for census-scale entity resolution research without privacy restrictions: a large-scale dataset generated by individual-based modelingGates Open Research10.12688/gatesopenres.15418.28(36)Online publication date: 18-Oct-2024
https://doi.org/10.12688/gatesopenres.15418.2
Haddock BPletcher ABlair-Stahn NKeyes OKappel MBachmeier SLutze SAlbright JBowman AKinuthia CBurke-Conte ZMudambi RFlaxman A(2024)Simulated data for census-scale entity resolution research without privacy restrictions: a large-scale dataset generated by individual-based modelingGates Open Research10.12688/gatesopenres.15418.18(36)Online publication date: 3-May-2024
https://doi.org/10.12688/gatesopenres.15418.1
Taylor IKaplan ABetancourt B(2024)Fast Bayesian Record Linkage for Streaming Data ContextsJournal of Computational and Graphical Statistics10.1080/10618600.2023.228357133:3(833-844)Online publication date: 3-Jan-2024
https://doi.org/10.1080/10618600.2023.2283571
Jugl MKirsten T(2024)Gecko: A Python library for the generation and mutation of realistic personal identification data at scaleSoftwareX10.1016/j.softx.2024.10184627(101846)Online publication date: Sep-2024
https://doi.org/10.1016/j.softx.2024.101846
Wu NVatsalan DKaafar MRamesh S(2023)Privacy-Preserving Record Linkage for Cardinality CountingProceedings of the 2023 ACM Asia Conference on Computer and Communications Security10.1145/3579856.3590338(53-64)Online publication date: 10-Jul-2023
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Binette OSteorts R(2022)(Almost) all of entity resolutionScience Advances10.1126/sciadv.abi80218:12Online publication date: 25-Mar-2022
https://doi.org/10.1126/sciadv.abi8021
Wu NVatsalan DVerma SKaafar M(2022)Fairness and Cost Constrained Privacy-Aware Record LinkageIEEE Transactions on Information Forensics and Security10.1109/TIFS.2022.319149217(2644-2656)Online publication date: 2022
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Aleshin-Guendel SSadinle M(2022)Multifile Partitioning for Record Linkage and Duplicate DetectionJournal of the American Statistical Association10.1080/01621459.2021.2013242118:543(1786-1795)Online publication date: 28-Jan-2022
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Rohde FFranke MSehili ZLablans MRahm E(2021)Optimization of the Mainzelliste software for fast privacy-preserving record linkageJournal of Translational Medicine10.1186/s12967-020-02678-119:1Online publication date: 15-Jan-2021
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