A fully spatial personalized differentially private mechanism to provide non-uniform privacy guarantees for spatial databases

Spatial databases are essential to applications in a wide variety of domains. One of the main privacy concerns when answering statistical queries, such as range counting queries, over a spatial database is that an adversary observing changes in query answers may be able to determine whether or not a particular geometric object is present in the database. Differential privacy addresses this concern by guaranteeing that the presence or absence of a geometric object has little effect on query answers. Most of the current differentially private mechanisms for spatial databases ignore the fact that privacy is personal and, thus, provide the same privacy protection for all geometric objects. However, some particular geometric objects may be more sensitive to privacy issues than others, requiring stronger differential privacy guarantees. In this paper, we introduce the concept of spatial personalized differential privacy for spatial databases where different geometric objects have different privacy protection requirements. Also, we present SPDP-PCE, a novel spatial personalized differentially private mechanism to answer range counting queries over spatial databases that fully considers the privacy protection requirements of geometric objects in the underlying geometric space in both steps of noise addition and consistency enforcement. Our experimental results on real datasets demonstrate the effectiveness of SPDP-PCE under various total privacy budgets, query shapes, and privacy level distributions.     

基于差分隐私的社交网络位置近邻查询方法

针对社交网络中近邻位置查询时个人位置隐私泄漏的问题,采用地理不可区分性机制对位置数据添加随机噪声,提出了一种隐私预算分配方法。首先,对空间区域进行网格化分割,根据用户在不同区域的位置访问量来个性化分配隐私预算;然后,为了解决在扰动位置数据集中近邻查询命中率偏低的问题,提出了一种组合增量近邻查询（CINQ）算法,以扩大需求空间的检索范围,并利用组合查询过滤冗余数据。在仿真实验中,与SpaceTwist算法相比,CINQ算法的查询命中率提高了13.7个百分点。实验结果表明,CINQ算法有效解决了因为查询目标的位置扰动所带来的查询命中率偏低问题,适用于社交网络应用中扰动位置的近邻查询。     

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。     

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%.     

Multi-agent reinforcement learning via knowledge transfer with differentially private noise

In multi-agent reinforcement learning, transfer learning is one of the key techniques used to speed up learning performance through the exchange of knowledge among agents. However, there are three challenges associated with applying this technique to real-world problems. First, most real-world domains are partially rather than fully observable. Second, it is difficult to pre-collect knowledge in unknown domains. Third, negative transfer impedes the learning progress. We observe that differentially private mechanisms can overcome these challenges due to their randomization property. Therefore, we propose a novel differential transfer learning method for multi-agent reinforcement learning problems, characterized by the following three key features. First, our method allows agents to implement real-time knowledge transfers between each other in partially observable domains. Second, our method eliminates the constraints on the relevance of transferred knowledge, which expands the knowledge set to a large extent. Third, our method improves robustness to negative transfers by applying differentially exponential noise and relevance weights to transferred knowledge. The proposed method is the first to use the randomization property of differential privacy to stimulate the learning performance in multi-agent reinforcement learning system. We further implement extensive experiments to demonstrate the effectiveness of our proposed method.     

一种面向决策树构建的差分隐私保护算法

对差分隐私的基本概念和实现方法进行了介绍,提出了一种用于决策树分析的差分隐私保护数据发布算法.该算法首先将数据完全泛化,然后在给定的隐私保护预算下采用指数机制将数据逐步精确化,最后根据拉普拉斯机制向数据中加入噪声,保证整个算法过程满足差分隐私保护要求;对指数机制中方案选择的方法进行了有效的改进.相对于已有的算法,本算法可在给定的隐私保护预算下使数据泛化程度更小,使所发布数据建立的决策树模型具有更高的分类准确率.实验结果验证了本算法的有效性和相对于其他算法的优越性.     

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.     

Predictable performance and high query concurrency for data analytics

Conventional data warehouses employ the query-at-a-time model, which maps each query to a distinct physical plan. When several queries execute concurrently, this model introduces contention and thrashing, because the physical plans??unaware of each other??compete for access to the underlying I/O and computation resources. As a result, while modern systems can efficiently optimize and evaluate a single complex data analysis query, their performance suffers significantly and can be highly erratic when multiple complex queries run at the same time. We present in this paper Cjoin, a new design that substantially improves throughput in large-scale data analytics systems processing many concurrent join queries. In contrast to the conventional query-at-a-time model our approach employs a single physical plan that shares I/O, computation, and tuple storage across all in-flight join queries. We use an ??always on?? pipeline of non-blocking operators, managed by a controller that continuously examines the current query mix and optimizes the pipeline on the fly. Our design enables data analytics engines to scale gracefully to large data sets, provide predictable execution times, and reduce contention. We implemented Cjoin as an extension to the PostgreSQL DBMS. This prototype outperforms conventional commercial systems by an order of magnitude for tens to hundreds of concurrent queries.     

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.     

SMT求解器理论组合技术研究

可满足模理论(SMT)求解器是计算机科学中用来判定一阶逻辑公式可满足性的程序,是许多形式化方法的验证引擎.理论求解器实现了SMT基于不同理论背景的求解过程,然而实际问题常以多个理论为背景.因此,本文重点介绍理论组合判定方法,概述SMT求解器的发展现状,并分析了几个主流SMT求解器理论组合判定关键技术.通过对照实验,评估...     

树索引数据差分隐私预算分配方法

树索引空间数据进行差分隐私保护时需要产生噪声,针对现有差分隐私预算采取均匀分配方式,普通用户无法个性化选择的问题,提出等差数列分配法和等比数列分配法两种分配隐私预算策略。首先,利用树结构索引空间数据;然后,用户根据隐私保护度的需要和查询精确度的需要,个性化设置相邻两层分配的隐私预算的差值或比值,动态调整隐私预算;最后,隐私预算分配给树的每一层,实现了个性化按需分配方式。理论分析和实验结果表明,与均匀分配方式相比,这两种方法分配隐私预算更加灵活,且等比数列分配法优于等差数列分配法。     

可穿戴设备的数值型流数据差分隐私均值发布

可穿戴设备实时产生的用户健康数据（如心率、血糖等）对健康监测及疾病诊断具有重大意义，然而健康数据属于用户的隐私信息。针对可穿戴设备的数值型流数据均值发布，为防止用户的隐私信息泄漏，提出一种基于自适应采样的可穿戴设备差分隐私均值发布方法。首先，引入适应可穿戴设备流数据均值波动小这一特点的全局敏感度；然后，采用基于卡尔曼滤波调整误差的自适应采样的方式分配隐私预算，提高发布数据的可用性。在发布两种健康数据的实验中，所提方法在隐私预算为0.1时，即高隐私保护强度下，在心率和血糖数据集上的平均相对误差(MRE)分别为0.01和0.08，相较于差分隐私时序监测的滤波和自适应采样（FAST）算法分别降低了36%和33%。所提的均值发布方法能够提高可穿戴设备均值流数据发布的可用性。     

基于差分隐私保护技术的多方求和查询方法

差分隐私保护技术因其不需要攻击者先验知识的假设,而被认为是一种非常可靠的保护机制。然而,差分隐私保护技术很少在多方环境下使用。鉴于此,将差分隐私保护技术用于多方环境下数据求和查询问题,详细讨论了如何通过加入噪声的方法来实现数据的保护,并证明该方法安全性。     

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.     

利用SMT约束分解方法求解RTL可满足性问题

随着集成电路技术与工艺的不断发展,目前工业界所采用的形式验证工具已很难适应集成电路规模的飞速增长.为了对RTL电路的可满足性问题进行形式验证,提出基于超图划分的约束分解实现可满足性模理论(SMT)求解的分级验证方法.通过分析RTL电路的结构约束,对约束集合中的元素和相关变量进行约束建模,并构建带有合适权重的超图模型;利用超图划分的机制寻找带有最小割集的等量划分,实现约束分解,完成RTL电路的定界模型检验.实验结果表明,该方法能够减小处理问题的规模和求解过程中的搜索空间,提高验证效率.     

HIFUN - a high level functional query language for big data analytics

We present a high level query language, called HIFUN, for defining analytic queries over big datasets, independently of how these queries are evaluated. An analytic query in HIFUN is defined to be a well-formed expression of a functional algebra that we define in the paper. The operations of this algebra combine functions to create HIFUN queries in much the same way as the operations of the relational algebra combine relations to create algebraic queries. The contributions of this paper are: (a) the definition of a formal framework in which to study analytic queries in the abstract; (b) the encoding of a HIFUN query either as a MapReduce job or as an SQL group-by query; and (c) the definition of a formal method for rewriting HIFUN queries and, as a case study, its application to the rewriting of MapReduce jobs and of SQL group-by queries. We emphasize that, although theoretical in nature, our work uses only basic and well known mathematical concepts, namely functions and their basic operations.     

RTL验证中的混合可满足性求解

RTL混合可满足性求解方法分为基于可满足性模理论(SMT)和基于电路结构搜索两大类.前者主要使用逻辑推理的方法,目前已在处理器验证中得到了广泛的应用,主要得益于SMT支持用于描述验证条件的基础理论;后者能够充分地利用电路中的约束信息,因而求解效率较高.介绍了每一大类中的典型研究及其所采用的重要策略,以及RTL可满足性求解方面的研究进展.     

有效的基于滑动窗口数据流直方图方法

差分隐私作为现在的一种隐私保护机制得到了广泛的应用.目前虽然存在着很多种静态数据集上的直方图发布方法,但是对于数据流环境下的基于滑动窗口直方图发布方法较少,并且面临着直方图的发布误差较高的问题.对于此问题,提出了一种适用于滑动窗口模型的数据流差分隐私直方图发布算法(histogram pub-lishing algorithm for sliding window model,HPA-SW).该算法首先基于数据分块的思想来把一个滑动窗口划分为k个子块,并通过该参数来控制和调节数据直方图的统计误差;随后,该算法通过比较相邻两个直方图数据分布的差异来优化当前窗口的隐私预算分配,从而快速计算出局部最优直方图.为了验证算法的有效性,首先通过严格的理论推导证实了所设计的算法符合差分隐私要求,并且其近似误差不超过W/2k.其次,通过在真实数据集合上的实验对比,显示了该算法的发布误差较低,比SSHP算法降低了50％.     

对混合属性数据表可行的差分隐私保护方法

为加强隐私保护和提高数据可用性,提出一种可对混合属性数据表执行差分隐私的数据保护方法。该方法首先采用ICMD（insensitive clustering for mixed data）聚类算法对数据集进行聚类匿名,然后在此基础上进行
-差分隐私保护。ICMD聚类算法对数据表中的分类属性和数值属性采用不同方法计算距离和质心,并引入全序函数以满足执行差分隐私的要求。通过聚类,实现了将查询敏感度由单条数据向组数据的分化,降低了信息损失和信息披露的风险。最后实验结果表明了该方法的有效性。