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Applying Differential Privacy with Sparse Vector Technique

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자료유형E-Book
개인저자Chen, Yan.
단체저자명Duke University. Computer Science.
서명/저자사항Applying Differential Privacy with Sparse Vector Technique.
발행사항[S.l.] : Duke University., 2018
발행사항Ann Arbor : ProQuest Dissertations & Theses, 2018
형태사항139 p.
소장본 주기School code: 0066.
ISBN9780355892017
일반주기 Source: Dissertation Abstracts International, Volume: 79-10(E), Section: B.
Adviser: Ashwin Machanavajjhala.
요약In today's fast-paced developing digital world, a wide range of services such as web services, social networks, and mobile devices collect a large amount of personal data from their users. Although sharing and mining large-scale personal data c
요약Differential privacy has emerged as a de facto standard for analyzing sensitive data with strong provable privacy guarantees for individuals. There is a rich literature that has led to the development of differentially private algorithms for num
요약First, we revisit the original Sparse Vector Technique and its variants, proving that many of its variants violate the definition of differential privacy. Furthermore, we design an attack algorithm demonstrating that an adversary can reconstruct
요약Next, we utilize the original Sparse Vector Technique primitive to design new solutions for practical problems. We propose the first algorithms to publish regression diagnostics under differential privacy for evaluating regression models. Speci
요약We then make use of Sparse Vector Technique as a key primitive to design a novel algorithm for differentially private stream processing, supporting queries on streaming data. This novel algorithm is data adaptive and can simultaneously support m
일반주제명Computer science.
언어영어
기본자료 저록Dissertation Abstracts International79-10B(E).
Dissertation Abstract International
대출바로가기http://www.riss.kr/pdu/ddodLink.do?id=T14997071

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