Differentially Private Multidimensional Data Publication
Differentially Private Multidimensional Data Publication作者机构：SchoolofMathematicalSciencesFudanUniversityShanghai201203China DepartmentofComputerScienceandEngineeringShanghaiJiaoTongUniversityShanghai200240China InformationSecurityLaboratoryNationalDisasterRecoveryTechnologyEngineeringLa-boratoryBeijingUniversityofPostaandTelecommunicationBeijing100876China
出 版 物：《China Communications》 (中国通信（英文版）)
年 卷 期：2014年第11卷第A01期
页 面：79-85页
核心收录：
学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 08[工学] 0815[工学-水利工程]
基 金：the National Basic Research Program of China under Grant 2013CB338004 Doctoral Program of Higher Education of China under Grant No.20120073120034 National Natural Science Foundation of China under Grants No.61070204 61101108 and National S&T Major Program under Grant No.2011ZX03002-005-01
主 题：data publication differential privacy data utility
摘 要：Multidimensional data provides enormous opportunities in a variety of applications. Recent research has indicated the failure of existing sanitization techniques （e.g., k-anonymity） to provide rigorous privacy guarantees. Privacy- preserving multidimensional data publishing currently lacks a solid theoretical foundation. It is urgent to develop new techniques with provable privacy guarantees, e-Differential privacy is the only method that can provide such guarantees. In this paper, we propose a multidimensional data publishing scheme that ensures c-differential privacy while providing accurate results for query processing. The proposed solution applies nonstandard wavelet transforms on the raw multidimensional data and adds noise to guarantee c-differential privacy. Then, the scheme processes arbitrarily queries directly in the noisy wavelet- coefficient synopses of relational tables and expands the noisy wavelet coefficients back into noisy relational tuples until the end result of the query. Moreover, experimental results demonstrate the high accuracy and effectiveness of our approach.
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