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PRIVACY PRESERVING INDUCTION OF DECISION TREES FROM GEOGRAPHICALLY DISTRIBUTED DATABASES

KINSEY, MICHAEL LOY

Abstract Details

2005, MS, University of Cincinnati, Engineering : Computer Science.
When applying contemporary decision tree construction techniques, such as the commonly used ID3 algorithm, to situational input stored in geographically distributed databases, several problems can arise from the construction process. Previously developed construction algorithms require a complete and local dataset from which a decision tree can be built. This means that all data stored in distributed databases must be transferred to a common site. The danger in this transfer is obvious if the data itself is innately sensitive. The privacy preserving methods described in this thesis will nullify all problems posed from the need to transfer distributed data to a common location before decision tree construction can begin.
Dr. Raj Bhatnagar (Advisor)
83 p.

Recommended Citations

Citations

  • KINSEY, M. L. (2005). PRIVACY PRESERVING INDUCTION OF DECISION TREES FROM GEOGRAPHICALLY DISTRIBUTED DATABASES [Master's thesis, University of Cincinnati]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1123855448

    APA Style (7th edition)

  • KINSEY, MICHAEL. PRIVACY PRESERVING INDUCTION OF DECISION TREES FROM GEOGRAPHICALLY DISTRIBUTED DATABASES. 2005. University of Cincinnati, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=ucin1123855448.

    MLA Style (8th edition)

  • KINSEY, MICHAEL. "PRIVACY PRESERVING INDUCTION OF DECISION TREES FROM GEOGRAPHICALLY DISTRIBUTED DATABASES." Master's thesis, University of Cincinnati, 2005. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1123855448

    Chicago Manual of Style (17th edition)