Privacy-preserving data publishing is an important problem that has been the focus of
extensive study. The state-of-the-art solution for this problem is differential privacy, which …

We propose a general approach for differentially private synthetic data generation, that
consists of three steps:(1) select a collection of low-dimensional marginals,(2) measure …

Privacy preserving in data release and mining is a hot topic in the information security field
currently. As a new privacy notion, differential privacy (DP) has grown in popularity recently …

摘要数据发布与数据挖掘中的隐私保护问题是目前信息安全领域的一个研究热点.
作为一种严格的和可证明的隐私定义, 差分隐私近年来受到了极大关注并被广泛研究 …

Given a set D of tuples defined on a domain Omega, we study differentially private
algorithms for constructing a histogram over Omega to approximate the tuple distribution in …

Many privacy mechanisms reveal high-level information about a data distribution through
noisy measurements. It is common to use this information to estimate the answers to new …

Releasing high-dimensional data enables a wide spectrum of data mining tasks. Yet,
individual privacy has been a major obstacle to data sharing. In this paper, we consider the …

摘要随着数据分析和发布等应用需求的出现和发展, 如何保护隐私数据和防止敏感信息泄露成为
当前面临的重大挑战. 基于k-匿名或者划分的隐私保护方法, 只适应特定背景知识下的攻击而 …

Differentially private algorithms for answering sets of predicate counting queries on a
sensitive database have many applications. Organizations that collect individual-level data …

Differential privacy is a robust privacy standard that has been successfully applied to a
range of data analysis tasks. We describe the matrix mechanism, an algorithm for answering …