基于K-匿名的轨迹隐私保护相关技术研究

如何在保证轨迹数据可用性前提下,最大程度保护敏感信息不被泄露,是位置服务中的重要问题。基于泛化模糊思想的隐私保护技术是保护位置服务中轨迹隐私信息泄露的有效方法之一。阐述了轨迹隐私保护问题的内涵,归纳了轨迹可能遭受的攻击类型,分析了常用基于K匿名技术,比较了轨迹数据质量度量方法,指出K匿名模型应用到轨迹隐私保护问题的热点研究方向。     

支持二分图的匿名隐私保护系统的设计与实现

为了解决社会网络中二分图数据发布中的隐私保护问题,既要保护隐私信息,又要尽可能保持匿名数据发布后的可用性,使用二分图匿名的k-分组算法,设计和实现了一个二分图匿名的社会网络隐私保护系统,包括:1)上传二分图数据:为了满足不同用户的需求,用户将需要匿名的二分图数据统一保存为三个txt格式的文件,然后上传到系统;2)二分图匿名化:根据用户的隐私保护程度的需求,将二分图中的结点V和结点W进行安全分组处理,并生成匿名的二分图;3)匿名数据的输出:将匿名后的数据保存到用户选择的本地位置.     

数据要素的价值属性

数据作为核心生产要素,对于维持数字经济有序运行具有重要作用。在以数据为基础所构建的网络空间中,虚体与实体的有机结合使得主体之间可以进行跨时间与空间范围的沟通,也有助于通过创新迭代以维持供给与需求的动态匹配。数据是一剂良性催化剂,其价值体现在对传统的经济模式与社会生活进行多方面改进,涉及技术、劳动、交易等问题。因此,数据的价值分配是一项涉及面广、复杂性高的工作。数据产权包括原始数据生产者的使用权、隐私权以及数据机构的获益权,数据贡献表现为追踪市场趋势、生产要素联动、敏捷创新常态化,数据收益可以区分为存量式收益和增量式收益两类。     

基于SE-DEA和Tobit模型的陕西省物流效率研究

经过查找《统计年鉴》和《国民经济与社会发展统计公报》,搜集了2018年陕西10个城市投入产出指标的相关数据和环境因素影响指标的原始数据(表略)。1.DEA和SE-DEA的实证分析考虑决策单元的个数大于等于投入和产出指标个数之和的2倍之后,选取的10个决策单元,3个投入指标和2个产出指标,使得测算结果具有科学性和可用性。     

浅谈面向数据库应用的隐私保护

在信息技术的支持下,数据发掘与发布相关问题,受到无线通信以及计算机网络领域相关研究人员的高度重视,面向数据库应用的隐私保护的实现,能够有效防范信息外漏,为隐私数据提供安全保障。文章就数据库应用中隐私保护的局限性进行分析,进一步从数据加密、数据失真和限制发布这几方面入手,探讨数据库技术应用于隐私保护中的现实情况,并对宏观形势下面向数据库应用的隐私保护展开研究,旨在把握数据库应用情况并确保隐私数据安全,仅供相关人员参考。     

基于文本挖掘与语义识别的用户消费行为分析

如何更好地利用来源错综复杂的数据,给企业带来相应效益是各企业亟待解决的问题。文章以京东商城空调类家电产品的用户评论数据为主要分析源,对用户行为数据进行文本挖掘找出用户的真实需求。对抓取的用户评论数据进行语义识别和词频统计,完成原始数据的去噪、降维等数据清理;建立商品目标集的特征属性表和概率模型,对待研数据进行聚类分析;最后,对聚类的结果进行时间序列分析,从而获得较有价值的结论,形成基于用户评论数据的建议、推断和决策。     

移动互联网上的个人隐私保护研究

随着移动互联网的迅猛发展,个人在使用智能移动终端的同时,也将隐私数据主动或被动的传到了网络之中,这些数据一旦泄露或被利用,小则暴露个人隐私,大则危害国家安全。介绍了隐私泄露的常见途径及危害,并给出了隐私保护的对策和建议。     

基于区块链的军事供应链数据隐私共享技术

首先研究了区块链的军事供应链应用价值,然后对区块链在美军军事供应链管理中的应用现状进行了概述,最后结合区块链和隐私计算技术,提出了基于区块链的军事供应链数据隐私共享构架,既可以满足军事供应链数据协同共享的需求,又可以满足军事供应链对安全隐私的需要。     

趣商业

数据隐私保密模式每个人都有隐私,每个单位都有重要数据,想长期保留而且永不愿被人知道该怎幺办?初创企业Annada科技(Annada.cn)有独门秘籍。Annada把隐私数据同时加密备份到多个网络硬盘上,而且分布在不同国家,一份数据多达10个国家、60个备份;如果还不满意,VIP服务可以再把数据放在不同国家、不同城市、不同银行的保险库里,以硬盘或者DVD形式加密储存,     

大数据安全与隐私保护方式研究

对大数据的安全与隐私问题进行介绍,在此基础上对基于大数据安全的隐私保护方式进行论述。     

Opinions about privacy: does the type of information used for marketing purposes make a difference?

The availability of personal information is at an all-time high. Technology has revolutionised the marketing industry, enabling marketers to achieve precise selectivity through database efforts. With this selectivity comes the responsibility of determining acceptable and unacceptable uses of personal information by businesses and nonprofits for marketing purposes. This paper explores opinions on information privacy by investigating the impact of education in the formulation of opinions on the use of personal information for marketing purposes. A single credit course in direct marketing does not change opinions regarding information privacy. But a series of credit marketing courses does reduce the level of restrictions desired on the availability and use of personal information for marketing. Thus, the direct marketing industry would be better served by promoting and demonstrating the ethical use of personal information through ethical marketing practices by all its practitioners rather than by attempting to educate the general public about the nature and uses of such information and data for marketing purposes. Copyright © 1999 Henry Stewart Publications     

A Review on Modal Clustering

In spite of the current availability of numerous methods of cluster analysis, evaluating a clustering configuration is questionable without the definition of a true population structure, representing the ideal partition that clustering methods should try to approximate. A precise statistical notion of cluster, unshared by most of the mainstream methods, is provided by the density‐based approach, which assumes that clusters are associated to some specific features of the probability distribution underlying the data. The non‐parametric formulation of this approach, known as modal clustering, draws a correspondence between the groups and the modes of the density function. An appealing implication is that the ill‐specified task of cluster detection can be regarded to as a more circumscribed problem of estimation, and the number of clusters is also conceptually well defined. In this work, modal clustering is critically reviewed from both conceptual and operational standpoints. The main directions of current research are outlined as well as some challenges and directions of further research.     

Managing compliance with privacy regulations through translation guardrails: A health information exchange case study

Information privacy is increasingly important in our digitally connected world, particularly in healthcare, and privacy regulations are ramping up to promote appropriate privacy practices. As a digital platform that enables healthcare providers to exchange protected health information (PHI), a health information exchange (HIE) is governed by health information privacy regulations. The challenge for HIEs is to operate in a way that will maximize information exchange while maintaining compliance with regulations that may constrain the sharing of PHI. Regulations impose a measure of universality through compliance requirements, while being flexible to allow adaptation to the local context. However, our longitudinal case study into the privacy policies of an HIE, demonstrates that the journey of privacy ideas from their original formulation in regulations, to their ultimate enactment in an organizational setting, is accompanied by translations, such that the final implementation may vary extensively from its original form. Such variability often results in interpretations that differ from what the regulators intended. Consequently, translation guardrails are necessary to protect against problematic translations of regulatory ideas which could lead to compliance issues and loss of platform participation. Our findings offer two contributions. First, we contribute to the compliance literature by explaining how guardrails can balance the use of permission and obligation schemas which are necessary to translate regulations into effective organizational policies for the success of HIEs and other information exchange platforms. Second, we contribute to extending translation theory by explaining how pragmatic reasoning schemas function as the mechanism through which translation of regulations occurs.     

Smoothing and Benchmarking for Small Area Estimation

Small area estimation is concerned with methodology for estimating population parameters associated with a geographic area defined by a cross-classification that may also include non-geographic dimensions. In this paper, we develop constrained estimation methods for small area problems: those requiring smoothness with respect to similarity across areas, such as geographic proximity or clustering by covariates, and benchmarking constraints, requiring weighted means of estimates to agree across levels of aggregation. We develop methods for constrained estimation decision theoretically and discuss their geometric interpretation. The constrained estimators are the solutions to tractable optimisation problems and have closed-form solutions. Mean squared errors of the constrained estimators are calculated via bootstrapping. Our approach assumes the Bayes estimator exists and is applicable to any proposed model. In addition, we give special cases of our techniques under certain distributional assumptions. We illustrate the proposed methodology using web-scraped data on Berlin rents aggregated over areas to ensure privacy.     

Revealing transactions data to third parties: Implications of privacy regimes for welfare in online markets

We examine the effects of privacy policies regarding transactions (e.g., price/quantity) data on online shopping platforms. Disclosure of transactions data induces consumer signaling behavior that affects merchant pricing decisions and the welfare of platform participants. A profit‐maximizing platform prefers the disclosure policy that maximizes total welfare. Although this policy benefits sophisticated consumers, it harms unsophisticated (myopic) consumers. Consequently, the welfare effects of alternative privacy policies, data breaches, deceptive privacy policies, and opt‐in/opt‐out requirements can differ sharply, depending on the level of consumer sophistication and on other factors such as the prevailing status quo.     

A critical overview of privacy-preserving approaches for collaborative forecasting

Cooperation between different data owners may lead to an improvement in forecast quality—for instance, by benefiting from spatiotemporal dependencies in geographically distributed time series. Due to business competitive factors and personal data protection concerns, however, said data owners might be unwilling to share their data. Interest in collaborative privacy-preserving forecasting is thus increasing. This paper analyzes the state-of-the-art and unveils several shortcomings of existing methods in guaranteeing data privacy when employing vector autoregressive models. The methods are divided into three groups: data transformation, secure multi-party computations, and decomposition methods. The analysis shows that state-of-the-art techniques have limitations in preserving data privacy, such as (i) the necessary trade-off between privacy and forecasting accuracy, empirically evaluated through simulations and real-world experiments based on solar data; and (ii) iterative model fitting processes, which reveal data after a number of iterations.     

A Survey of Differentially Private Regression for Clinical and Epidemiological Research

Differential privacy is a framework for data analysis that provides rigorous privacy protections for database participants. It has increasingly been accepted as the gold standard for privacy in the analytics industry, yet there are few techniques suitable for statistical inference in the health sciences. This is notably the case for regression, one of the most widely used modelling tools in clinical and epidemiological studies. This paper provides an overview of differential privacy and surveys the literature on differentially private regression, highlighting the techniques that hold the most relevance for statistical inference as practiced in clinical and epidemiological research. Research gaps and opportunities for further inquiry are identified.     

The Importance of Communicating Workplace Privacy Policies

Employers today are faced with the task of managing workplace privacy, dealing with potential litigation, preserving the confidentiality of company information while protecting the rights of the employee. Since September 11, 2001, employers have faced new challenges including the implementation of new laws and the development of more sophisticated technology. As a result, workplace policies dealing with privacy must be clearly and effectively communicated to all affected workers. The objective of this survey research project was to measure the general public’s attitudes and perceptions of the communication of work place privacy policies. An emphasis was placed on the collection of real world data from multiple business and organizational environments, as opposed to data obtained in a controlled experimental setting.     

Reasoning under uncertainty and multi-criteria decision making in data privacy

By means of an integration of decision theory and probabilistic models, we explore and develop methods for improving data privacy. Our work encompasses disclosure control tools in statistical databases and privacy requirements prioritization; in particular we propose a Bayesian approach for the on-line auditing in Statistical Databases and Pairwise Comparison Matrices for privacy requirements prioritization. The first approach is illustrated by means of examples in the context of statistical analysis on the census and medical data, where no salary (resp. no medical information), that could be related to a specific employee (resp. patient), must be released; the second approach is illustrated by means of examples, such as an e-voting system and an e-banking service that have to satisfy privacy requirements in addition to functional and security ones. Several fields in the social sciences, economics and engineering will benefit from the advances in this research area: e-voting, e-government, e-commerce, e-banking, e-health, cloud computing and risk management are a few examples of applications for the findings of this research.     

Measure of privacy in randomized response model

Randomized response (say, RR) techniques on survey are used for collecting data on sensitive issues while trying to protect the respondents’ privacy. The degree of confidentiality will clearly determine whether or not respondents choose to cooperate. There have been many proposals for privacy measures with very different implications for an optimal model design. These derived measures of protection privacy involve both conditional probabilities of being perceived as belonging to sensitive group, denoted as P(A|yes) and P(A|no). In this paper, we introduce an alternative criterion to measure privacy protection and reconsider and compare some RR models in the light of the efficiency/protection privacy. This measure is known to the respondents before they agree to use the RR model. This measure is helpful for choosing an optimal RR model in practice.