Repository logo
  • English
  • Català
  • Čeština
  • Deutsch
  • Español
  • Français
  • Gàidhlig
  • Latviešu
  • Magyar
  • Nederlands
  • Polski
  • Português
  • Português do Brasil
  • Suomi
  • Svenska
  • Türkçe
  • Қазақ
  • বাংলা
  • हिंदी
  • Ελληνικά
  • Yкраї́нська
  • Log In
    or
    New user? Click here to register.Have you forgotten your password?
Repository logo
  • PRISM

  • Communities & Collections
  • All of PRISM
  • English
  • Català
  • Čeština
  • Deutsch
  • Español
  • Français
  • Gàidhlig
  • Latviešu
  • Magyar
  • Nederlands
  • Polski
  • Português
  • Português do Brasil
  • Suomi
  • Svenska
  • Türkçe
  • Қазақ
  • বাংলা
  • हिंदी
  • Ελληνικά
  • Yкраї́нська
  • Log In
    or
    New user? Click here to register.Have you forgotten your password?
  1. Home
  2. Browse by Author

Browsing by Author "Sramka, Michal"

Now showing 1 - 1 of 1
Results Per Page
Sort Options
  • Loading...
    Thumbnail Image
    Item
    Open Access
    Utility of Knowledge Discovered from Sanitized Data
    (2008-09-30) Sramka, Michal; Safavi-Naini, Reihaneh; Denzinger,Jorg; Askari, Mina; Gao, Jie
    While much attention has been paid to data sanitization methods with the aim of protecting users’ privacy, far less emphasis has been put to the usefulness of the sanitized data from the view point of knowledge discovery systems. We consider this question and ask whether sanitized data can be used to obtain knowledge that is not defined at the time of the sanitization. We propose a utility function for knowledge discovery algorithms, which quantifies the value of the knowledge from a perspective of users of the knowledge. We then use this utility function to evaluate the usefulness of the extracted knowledge when knowledge building is performed over the original data, and compare it to the case when knowledge building is performed over the sanitized data. Our experiments use an existing cooperative learning model of knowledge discovery and medical data, anonymized and perturbed using two widely known sanitization techniques, called E-differential privacy and k-anonymity. Our experimental results show that although the utility of sanitized data can be drastically reduced and in some cases completely lost, there are cases where the utility can be preserved. This confirms our strategy to look at triples consisting of a utility function, a sanitization mechanism, and a knowledge discovery algorithm that are useful in practice. We categorize a few instances of such triples based on usefulness obtained from experiments over a single database of medical records. We discuss our results and show directions for future work.

Libraries & Cultural Resources

  • Contact us
  • 403.220.8895
Start Something.
  • Digital Privacy Statement
  • Privacy Policy
  • Website feedback

University of Calgary
2500 University Drive NW
Calgary Alberta T2N 1N4
CANADA

Copyright © 2023

The University of Calgary acknowledges the traditional territories of the people of the Treaty 7 region in Southern Alberta, which includes the Blackfoot Confederacy (comprised of the Siksika, Piikani, and Kainai First Nations), as well as the Tsuut’ina First Nation, and the Stoney Nakoda (including the Chiniki, Bearspaw and Wesley First Nations). The City of Calgary is also home to Metis Nation of Alberta, Region 3. The University of Calgary acknowledges the impact of colonization on Indigenous peoples in Canada and is committed to our collective journey towards reconciliation to create a welcome and inclusive campus that encourages Indigenous ways of knowing, doing, connecting and being.