التفاصيل البيبلوغرافية
| العنوان: |
A Differentially Private (Random) Decision Tree without Noise from k -Anonymity †. |
| المؤلفون: |
Waseda, Atsushi, Nojima, Ryo, Wang, Lihua |
| المصدر: |
Applied Sciences (2076-3417); Sep2024, Vol. 14 Issue 17, p7625, 15p |
| مصطلحات موضوعية: |
DECISION trees, LEAKS (Disclosure of information), MACHINE learning, PRIVACY, NOISE |
| مستخلص: |
This paper focuses on the relationship between decision trees, a typical machine learning method, and data anonymization. It is known that information leaked from trained decision trees can be evaluated using well-studied data anonymization techniques and that decision trees can be strengthened using k-anonymity and ℓ-diversity; unfortunately, however, this does not seem sufficient for differential privacy. In this paper, we show how one might apply k-anonymity to a (random) decision tree, which is a variant of the decision tree. Surprisingly, this results in differential privacy, which means that security is amplified from k-anonymity to differential privacy without the addition of noise. [ABSTRACT FROM AUTHOR] |
|
Copyright of Applied Sciences (2076-3417) is the property of MDPI and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) |
| قاعدة البيانات: |
Complementary Index |
Full Text Finder