상세정보
Export to Refworks부가기능
An Information Theoretic Approach for Privacy Preservation in Distance-based Machine Learning
상세 프로파일
| 자료유형 | 학위논문 |
|---|---|
| 서명/저자사항 | An Information Theoretic Approach for Privacy Preservation in Distance-based Machine Learning. |
| 개인저자 | Jimenez Gajardo, Abelino Enrique. |
| 단체저자명 | Carnegie Mellon University. Electrical and Computer Engineering. |
| 발행사항 | [S.l.]: Carnegie Mellon University., 2019. |
| 발행사항 | Ann Arbor: ProQuest Dissertations & Theses, 2019. |
| 형태사항 | 138 p. |
| 기본자료 저록 | Dissertations Abstracts International 81-05B. Dissertation Abstract International |
| ISBN | 9781687905529 |
| 학위논문주기 | Thesis (Ph.D.)--Carnegie Mellon University, 2019. |
| 일반주기 |
Source: Dissertations Abstracts International, Volume: 81-05, Section: B.
Advisor: Raj, Bhiksha. |
| 이용제한사항 | This item must not be sold to any third party vendors. |
| 요약 | As cloud-based services become increasingly popular as platforms for storage and computation, privacy issues relating to their use have become increasingly important. Much of the data stored on cloud platforms are private, belonging to individuals or institutions who often desire to utilize the facilities provided by these platforms, but, at the same time, do not desire to expose their data to the platform itself.Encrypting the data prior to storage on the cloud helps to protect private information. However, this causes problems if we need to perform computations on them, for instance, to train some machine learning algorithm. This requires the server to observe the content, so decryption is necessary. This gives rise to privacy concerns in different cloud computing settings. Several solutions based on cryptographic techniques have been proposed to address the issue. However, they have high computational cost and high bandwidth requirements, and in practice are difficult to scale.In this work, we propose an alternative approach. In this work we introduce a privacy mechanism based on limited leakage transformations which have two key properties:1. Individual transformed vectors are uninformative about their preimage |
| 일반주제명 | Engineering. Computer science. |
| 언어 | 영어 |
| 바로가기 | 
: 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
