slide1 l.
Download
Skip this Video
Loading SlideShow in 5 Seconds..
College of Information Science & Technology PowerPoint Presentation
Download Presentation
College of Information Science & Technology

Loading in 2 Seconds...

play fullscreen
1 / 13

College of Information Science & Technology - PowerPoint PPT Presentation


  • 193 Views
  • Uploaded on

University of Nebraska’s. College of Information Science & Technology. Privacy-preserving Data Mining of Medical Data using Data Separation Based Techniques. Gang Kou, Yi Peng, Yong Shi, and Zhengxin Chen. Data Mining and Knowledge Discovery.

loader
I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
capcha
Download Presentation

PowerPoint Slideshow about 'College of Information Science & Technology' - ilario


An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript
slide1

University of Nebraska’s

College of Information Science & Technology

privacy preserving data mining of medical data using data separation based techniques

Privacy-preserving Data Mining of Medical Data using Data Separation Based Techniques

Gang Kou, Yi Peng, Yong Shi, and Zhengxin Chen

slide3

Data Mining and Knowledge Discovery

“Knowledge Discovery is the non-trivial process of identifying valid, novel, potentially useful, and ultimately understandablepatterns in data. ”

— U. Fayyad, G. Piatetsky-Shapiro, and P. Smyth

Advances in Knowledge Discovery and Data Mining, AAAI/MIT Press, 1996

slide4

We are drowning in data, but starving for knowledge! Adopted from Jiawei Han, UIUC

Increasing potential

to support

business decisions

Knowledge

End User

Business

Analyst

Data Presentation

Visualization Techniques

Data Mining

Data

Analyst

Information Discovery

Data Exploration

Statistical Analysis, Querying and Reporting

Data Warehouses / Data Marts

OLAP, MDA

DBA

Data Sources

Paper, Files, Information Providers, Database Systems, OLTP

slide5

A Caveman's Wisdom

Source: The Futurist, December 1982, Tom Chalkley

Information is data that has been given context and is meaningful to a particular audience

Knowledge is information with a broader, general understanding of the subject matter

Wisdom is knowledge embedded with the reflection or application of that knowledge

slide6

Introduction

Data mining is concerned with the extraction of useful knowledge from various types of data.

Medical data mining has been a popular data mining topic of late. Compared with other data mining areas, medical data mining has some unique characteristics. Since medical files are related to human subjects, privacy concern is taken more seriously than other data mining tasks.

This paper applied data separation-based techniques to preserve privacy in classification of medical data.

We implement these two approaches using medical datasets from UCI KDD archive and report the experimental results.

slide7

Medical Datasets in this research

Wisconsin prognostic breast cancer dataset has 699 records and 9 variables. These records belong to either benign or malignant class. We create nine different sub-datasets by removing one variable at a time.

The heart-disease dataset has 797 records and 13 variables. These records belong to either heart-disease or normal class. This data was collected from Cleveland Clinic Foundation, Hungarian Institute of Cardiology, University Hospital of Zurich, and Long Beach V.A. Medical Center.

slide8

Privacy-preserving Data Mining

  • – Data obfuscation
  • Nobody sees the real data so it hide the protected information
  • Approaches– Randomly modify data– Swap values between records– Controlled modification of data to hide secrets
  • – Summarization
  • Only the needed facts are exposed
  • Approaches– Overall collection statistics– Limited query functionality
  • – Data separation
  • Data remains with trusted parties
  • Approaches– Vertical separation– Horizontal separation
slide9

Privacy-preserving Classification (Vertical) Process

Input: The Medical dataset M , each of the medical records has m attributes

Output: Average classification accuracies for benign and malignant of the dataset in 10-fold cross-validation; scores for all records; decision trees ensemble.

Step 1 Generate m subsets with one different attribute removed from M at each time.

Step 2 Training each subset with See5 with adaptive boosting and 10-fold cross validation to get m decision trees.

Step 3 Ensemble the final decision function D, via majority vote of the m decision trees from step 2.

Step 4 Classify M by the final decision function.

END

slide10

Privacy-preserving Classification (Horizontal) Process

Input: The Medical dataset from r different sources, M1, M2, Mr, each of the medical records has m attributes

Output: Average classification accuracies for Normal and Heart-disease of the dataset in 10-fold cross-validation; scores for all records; decision trees ensemble.

Step 1 Training r datasets with See5 with adaptive boosting and 10-fold cross validation to get r decision trees .

Step 2 Ensemble the final decision function D, via majority vote of the r decision trees from step 1.

Step 3 Classify all r datasets by the final decision function.

END

slide11

Privacy-preserving Data Mining

Vertical partition - each site uses a portion of the attributes to compute its results and the distributed results are assembled at a central trusted party using majority-vote ensemble method.

Horizontal partition - data are distributed among several sites. Each site computes its own data and a central party is responsible to integrate these results.

slide12

Conclusion

Privacy-preserving is an important issue in medical data mining. This paper investigates data separation techniques in medical data classification. The experiments demonstrate that data separation techniques can not only protect data privacy, but also increase classification accuracy sometimes (e.g., horizontally partitioned data).

slide13

University of Nebraska’s

Comment & Question谢谢大家!Thank You!