Anonymization by Local Recoding in Data with Attribute Hierarchical Taxonomies

Individual privacy will be at risk if a published data set is not properly de-identified. k-anonymity is a major technique to de-identify a data set. Among a number of k-anonymisation schemes, local recoding methods are promising for minimising the distortion of a k-anonymity view. This paper addresses two major issues in local recoding k-anonymisation in attribute hierarchical taxonomies. Firstly, we define a proper distance metric to achieve local recoding generalisation with small distortion. Secondly, we propose a means to control the inconsistency of attribute domains in a generalised view by local recoding. We show experimentally that our proposed local recoding method based on the proposed distance metric produces higher quality k-anonymity tables in three quality measures than a global recoding anonymisation method, Incognito, and a multidimensional recoding anonymisation method, Multi. The proposed inconsistency handling method is able to balance distortion and consistency of a generalised view.     

Sufficient dimension reduction for average causal effect estimation

Data Mining and Knowledge Discovery - A large number of covariates can have a negative impact on the quality of causal effect estimation since confounding adjustment becomes unreliable when the...     

On optimal rule discovery

In machine learning and data mining, heuristic and association rules are two dominant schemes for rule discovery. Heuristic rule discovery usually produces a small set of accurate rules, but fails to find many globally optimal rules. Association rule discovery generates all rules satisfying some constraints, but yields too many rules and is infeasible when the minimum support is small. Here, we present a unified framework for the discovery of a family of optimal rule sets and characterize the relationships with other rule-discovery schemes such as nonredundant association rule discovery. We theoretically and empirically show that optimal rule discovery is significantly more efficient than association rule discovery independent of data structure and implementation. Optimal rule discovery is an efficient alternative to association rule discovery, especially when the minimum support is low.     

外墙外保温聚苯板钢丝网架在施工中的改进措施

针对机械固定聚苯板钢丝网架外墙外保温（外饰面为面砖）在具体施工中遇到的质量问题,提出几点针对性的改进措施,为相关外保温工程的施工提供参考。     

带多软时间窗VRP及其禁忌搜索算法

分析了带多软时间窗VRP实际应用背景和特点,以使用的车辆数、行驶费用和偏离时间窗的惩罚费用为优化目标,结合车辆载重、最大路长等限制,建立该问题的数学模型,并设计求解该问题的自适应禁忌搜索算法。为增强算法的全局寻优能力,设计了多邻域结构并在算法中嵌入一种有限地接受不可行解的自适应机制。分别用文献中的算例和以Solomon标准算例为基础构建的新算例测试该算法,并将结果与其他方法进行对比分析。对比结果表明,所提出的算法性能较好,能在可接受的时间内求出运输成本更少、满意度更高的解。     

砂西中浅层复杂断块油藏精细描述与调整思路

复杂断块油藏在我国广泛分布,地位重要,但其油气分布复杂、开发调整难度较高。为探讨此类油藏调整思路,以柴达木盆地砂西N1—N21复杂断块油藏为例,采用以构造为核心的油藏精细描述,通过应力分析确定油藏构造格局、多方法联合精细地震解释和成图较好地体现了油藏构造特征。以油砂体预测为核心的油藏评价,通过沉积相研究预测砂体分布,基于构造格架以断块为单元预测油砂体分布,进而以油砂体为单元进行油藏评价。以增加动用程度、完善注采关系,特别是以主力小层的注采完善为核心的油藏开发调整方向,适应于该类油藏的特点,为稳定油藏产量,提高采收率指明了方向。     

地下建筑专用全新风无级调载除湿机研制

分析了普通除湿机用于地下建筑全新风系统存在的问题,指出设计全新风除湿机要解决的关键问题,介绍了样机设计方案及测试结果。     

Information based data anonymization for classification utility

Anonymization is a practical approach to protect privacy in data. The major objective of privacy preserving data publishing is to protect private information in data whereas data is still useful for some intended applications, such as building classification models. In this paper, we argue that data generalization in anonymization should be determined by the classification capability of data rather than the privacy requirement. We make use of mutual information for measuring classification capability for generalization, and propose two k-anonymity algorithms to produce anonymized tables for building accurate classification models. The algorithms generalize attributes to maximize the classification capability, and then suppress values by a privacy requirement k (IACk) or distributional constraints (IACc). Experimental results show that algorithm IACk supports more accurate classification models and is faster than a benchmark utility-aware data anonymization algorithm.     

(<Emphasis Type="Italic">α</Emphasis>, <Emphasis Type="Italic">k</Emphasis>)-anonymous data publishing

Privacy preservation is an important issue in the release of data for mining purposes. The k-anonymity model has been introduced for protecting individual identification. Recent studies show that a more sophisticated model is necessary to protect the association of individuals to sensitive information. In this paper, we propose an (α, k)-anonymity model to protect both identifications and relationships to sensitive information in data. We discuss the properties of (α, k)-anonymity model. We prove that the optimal (α, k)-anonymity problem is NP-hard. We first present an optimal global-recoding method for the (α, k)-anonymity problem. Next we propose two scalable local-recoding algorithms which are both more scalable and result in less data distortion. The effectiveness and efficiency are shown by experiments. We also describe how the model can be extended to more general cases.     

Synthesis and characterization of（S）-2-benzyloxyl propanal

（S）-2-苄氧基丙醛是有机合成及制药的重要中间体,本文以（S）-乳酸乙酯为原料,经四氢吡咯胺化、氯化苄醚化、红铝还原反应合成了（S）-2-苄氧基丙醛,考察了胺化的反应时间、反应温度和原料的摩尔比对反应收率的影响。结果表明：当胺化反应时间为75 h,最佳的反应温度为25 ℃,四氢吡咯与（S）-乳酸乙酯的摩尔比为1.4∶1时,胺化反应收率为94%,三步总收率为61.8%,采用GC-MS、IR、1H NMR表征确定了产物结构,研究结果为（S）-2-苄氧基丙醛的工业化生产奠定了基础。