差分隐私二维数据流统计发布

目前关于差分隐私数据流统计发布的研究仅考虑一维数据流,其方法无法直接用于解决二维数据流统计发布中可能存在的隐私泄露问题.针对此问题,首先提出面向固定长度二维数据流的差分隐私统计发布算法--PTDSS算法.该算法通过单次线性扫描数据流,以较低空间消耗计算出满足一定条件的二维数据流元组的统计频度,并经过敏感度分析添加适量的噪声使其满足差分隐私要求;接着在PTDSS算法的基础上,利用滑动窗口机制,设计出面向任意长度二维数据流的差分隐私连续统计发布算法--PTDSS-SW.理论分析与实验结果表明,所提算法可安全地实现二维数据流统计发布的隐私保护,同时统计发布结果的相对误差在10%~95%.     

Differentially private high-dimensional data publication via grouping and truncating techniques

The count of one column for high-dimensional datasets, i.e., the number of records containing this column, has been widely used in numerous applications such as analyzing popular spots based on check-in location information and mining valuable items from shopping records. However, this poses a privacy threat when directly publishing this information. Differential privacy (DP), as a notable paradigm for strong privacy guarantees, is thereby adopted to publish all column counts. Prior studies have verified that truncating records or grouping columns can effectively improve the accuracy of published results. To leverage the advantages of the two techniques, we combine these studies to further boost the accuracy of published results. However, the traditional penalty function, which measures the error imported by a given pair of parameters including truncating length and group size, is so sensitive that the derived parameters deviate from the optimal parameters significantly. To output preferable parameters, we first design a smart penalty function that is less sensitive than the traditional function. Moreover, a two-phase selection method is proposed to compute these parameters efficiently, together with the improvement in accuracy. Extensive experiments on a broad spectrum of real-world datasets validate the effectiveness of our proposals.     

Differentially private multidimensional data publishing

Various organizations collect data about individuals for various reasons, such as service improvement. In order to mine the collected data for useful information, data publishing has become a common practice among those organizations and data analysts, research institutes, or simply the general public. The quality of published data significantly affects the accuracy of the data analysis and thus affects decision making at the corporate level. In this study, we explore the research area of privacy-preserving data publishing, i.e., publishing high-quality data without compromising the privacy of the individuals whose data are being published. Syntactic privacy models, such as k-anonymity, impose syntactic privacy requirements and make certain assumptions about an adversary’s background knowledge. To address this shortcoming, we adopt differential privacy, a rigorous privacy model that is independent of any adversary’s knowledge and insensitive to the underlying data. The published data should preserve individuals’ privacy, yet remain useful for analysis. To maintain data utility, we propose DiffMulti, a workload-aware and differentially private algorithm that employs multidimensional generalization. We devise an efficient implementation to the proposed algorithm and use a real-life data set for experimental analysis. We evaluate the performance of our method in terms of data utility, efficiency, and scalability. When compared to closely related existing methods, DiffMulti significantly improved data utility, in some cases, by orders of magnitude.     

Differentially private random decision forests using smooth sensitivity

We propose a new differentially-private decision forest algorithm that minimizes both the number of queries required, and the sensitivity of those queries. To do so, we build an ensemble of random decision trees that avoids querying the private data except to find the majority class label in the leaf nodes. Rather than using a count query to return the class counts like the current state-of-the-art, we use the Exponential Mechanism to only output the class label itself. This drastically reduces the sensitivity of the query – often by several orders of magnitude – which in turn reduces the amount of noise that must be added to preserve privacy. Our improved sensitivity is achieved by using “smooth sensitivity”, which takes into account the specific data used in the query rather than assuming the worst-case scenario. We also extend work done on the optimal depth of random decision trees to handle continuous features, not just discrete features. This, along with several other improvements, allows us to create a differentially private decision forest with substantially higher predictive power than the current state-of-the-art.     

Differentially private nonlinear observer design using contraction analysis

Real‐time information processing applications such as those enabling a more intelligent infrastructure are increasingly focused on analyzing privacy‐sensitive data obtained from individuals. To produce accurate statistics about the habits of a population of users of a system, this data might need to be processed through model‐based estimators. Moreover, models of population dynamics, originating for example from epidemiology or the social sciences, are often necessarily nonlinear. Motivated by these trends, this paper presents an approach to design nonlinear privacy‐preserving model‐based observers, relying on additive input or output noise to give differential privacy guarantees to the individuals providing the input data. For the case of output perturbation, contraction analysis allows us to design convergent observers as well as set the level of privacy‐preserving noise appropriately. Two examples illustrate the proposed approach: estimating the edge formation probabilities in a social network using a dynamic stochastic block model, and syndromic surveillance relying on an epidemiological model.     

基于箱聚类的差分隐私直方图发布方法研究

在现有的基于差分隐私保护的直方图发布聚类处理算法中,没有算法考虑对方差较小与方差较大的直方图计数集加以区别对待,从而在处理方差较小的直方图计数集时造成算法复杂度过大.针对方差较小的直方图计数集,提出一种基于临近箱计数差值的分割策略.首先,通过计算相邻单位箱计数的差值确定分割边界;然后,根据重构误差与加噪误差的总量变化判断每次分割的可行性;最后,通过理论分析和实验仿真,该算法在保证发布数据准确度的同时,极大地提高了算法效率,从而验证了该算法的有效性.     

基于生成对抗网络的文本序列数据集脱敏

基于生成对抗网络和差分隐私提出一种文本序列数据集脱敏模型,即差分隐私文本序列生成网络（DP-SeqGAN）。DP-SeqGAN通过生成对抗网络自动提取数据集的重要特征并生成与原数据分布接近的新数据集,基于差分隐私对模型做随机加扰以提高生成数据集的隐私性,并进一步降低鉴别器过拟合。DP-SeqGAN 具有直观通用性,无须对具体数据集设计针对性脱敏规则和对模型做适应性调整。实验表明,数据集经DP-SeqGAN脱敏后其隐私性和可用性明显提升,成员推断攻击成功率明显降低。     

DP-UserPro: differentially private user profile construction and publication

User profiles are widely used in the age of big data. However, generating and releasing user profiles may cause serious privacy leakage, since a large number of personal data are collected and analyzed. In this paper, we propose a differentially private user profile construction method DP-UserPro, which is composed of DP-CLIQUE and privately top-k tags selection. DP-CLIQUE is a differentially private high dimensional data cluster algorithm based on CLIQUE. The multidimensional tag space is divided into cells, Laplace noises are added into the count value of each cell. Based on the breadthfirst-search, the largest connected dense cells are clustered into a cluster. Then a privately top-k tags selection approach is proposed based on the score function of each tag, to select the most important k tags which can represent the characteristics of the cluster. Privacy and utility of DP-UserPro are theoretically analyzed and experimentally evaluated in the last. Comparison experiments are carried out with Tag Suppression algorithm on two real datasets, to measure the False Negative Rate (FNR) and precision. The results show that DP-UserPro outperforms Tag Suppression by 62.5% in the best case and 14.25% in the worst case on FNR, and DP-UserPro is about 21.1% better on precision than that of Tag Suppression, in average.     

一种可控的直方图均衡算法

本文提出一种在传统直方图中利用细节信息的可调的直方图均衡算法。该算法不但能够有效的提高图像的对比度,而且还能增强图像的细节。这种算法可以采用各种不同的边缘信息如:Laplacian、Sobel等。实验证明该方法对增强图像是很有效的。     

一种可控的直方图均衡算法

本文提出一种在传统直方图中利用细节信息的可调的直方图均衡算法.该算法不但能够有效的提高图像的对比度,而且还能增强图像的细节.这种算法可以采用各种不同的边缘信息如Laplacian、Sobel等.实验证明该方法对增强图像是很有效的.     

限定误差直方图的一个改进算法

直方图是许多商用数据库系统中最常用的一种估算查询结果大小的方法。限定误差的直方图是以任意给定的误差作为前提,生成满足误差要求的直方图。文章提出了一种新的限定误差直方图的算法。在限定相同误差及时间复杂度的前提下,生成的直方数会更少。