Différences individuelles au niveau des croyances erronées à l'égard des inquiétudes.

Erroneous beliefs have been identified as key variables related to the development and maintenance of excessive worry. For example, people presenting a high level of worry often believe that worrying helps them to prevent the occurrence of future negative events or translates a positive characteristic of their personality. They thus develop excessive and uncontrollable worries in order to maintain the perceived advantages. The factors related to the development and the maintenance of erroneous beliefs about worry are however unknown. The aim of this study was to verify the relationship between erroneous beliefs and personal variables (i.e., gender, worry themes, and predominant types of worries) in a sample of 544 participants. Analyses showed that beliefs differed significantly according to the gender, but not according to the predominant types of worries. School concerns were significantly associated with the belief that worry helps to motivate. This study brings preliminary useful information to better understand the presence of erroneous beliefs about worries and suggests conducting further researches using other personal variables and clinical samples. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

云存储中一种支持可验证的模糊查询加密方案

针对当前可查询加密方案大多不支持模糊查询的不足,并且无法应对恶意服务器的威胁,云计算亟需为用户提供一种允许拼写错误并且可以验证查询结果正确性的加密方案。同时考虑到云存储中数据经常更新,提出一种动态云存储中支持可验证的模糊查询加密方案。该方案通过编辑距离生成模糊关键词集,并基于伪随机函数、随机排列函数等技术构建安全索引,从而保护用户的数据隐私。通过RSA累加器和哈希函数验证查询结果的正确性,用于检测恶意攻击者的非法行为。安全分析证明该方案能够保护用户的隐私安全,并具有可验证性。实验结果表明该方案具有可行性与有效性。     

医疗信息隐私保护的研究进展

近年来,医疗数据的泄露事件频发,人们越来越关注医疗数据的信息安全问题。医疗信息隐私保护的相关研究也在逐渐增多,但是文献回顾的文章十分匮乏。本文首先厘清了隐私、患者隐私和患者隐私权的相关概念,然后通过梳理医疗隐私保护伦理、法律法规及隐私保护技术三个方面的成果,阐述当前的研究进展,最后,对医疗隐私保护研究中存在的问题进行简要总结,以期为后续研究提供参考。     

支持本地化差分隐私保护的k-modes聚类方法

如何在保护数据隐私的同时进行可用性的数据挖掘已成为热点问题。鉴于在很多实际应用场景中,很难找到一个真正可信的第三方对用户的敏感数据进行处理,文中首次提出了一种支持本地化差分隐私技术的聚类方案——LDPK-modes(Local Differential Privacy K-modes)。与传统的基于中心化差分隐私的聚类算法相比,其不再需要一个可信的第三方对数据进行收集和处理,而由用户担任数据隐私化的工作,极大地降低了第三方窃取用户隐私的可能性。用户使用满足本地d-隐私(带有距离度量的本地差分隐私技术)定义的随机响应机制对敏感数据进行扰动,第三方收集到用户扰动数据后,恢复其统计特征,生成合成数据集,并进行k-modes聚类。在聚类过程中,将数据集上频繁出现的特征分配给初始聚类中心点,进一步提高了聚类结果的可用性。理论分析和实验结果表明了LDPK-modes的隐私性和聚类可用性。     

无线局域网的安全性研究

无线局域网已经成为网络体系结构的重要组成部分,同时无线局域网的安全也越来越受到人们的重视。该文重点介绍了IEEE802.11的安全机制,分析其安全缺陷,并对未来无线局域网在安全方面提出了改进方案。     

Surveillance and privacy – Beyond the panopticon. An exploration of 720-degree observation in level 3 and 4 vehicle automation

On the path to high-level vehicle automation, the degree of surveillance both inside and outside the car increases significantly. Consequently, ethical considerations are becoming central to questions around surveillance regimes and data privacy implicit in level 3 and 4 vehicle automation. In this paper, we focus on outputs from the EU Horizon 2020 project Vision Inspired Driver Assistance Systems (VI-DAS). In particular, we assess the VI-DAS 720-degree observation technology, critical to ensuring a safe Human Machine Interaction (HMI), from multiple theoretical perspectives to contribute to a better understanding of the phenomena of privacy. As a synonym for surveillance, we started our evaluation with Bentham's ideation of the panopticon. From there, it is a relatively short step to radical Foucauldian critiques that offered more dystopian technologies of power. However, both theorems demonstrate a limited understanding of the issue of data privacy in the context of safe transportation along the evolution of highly automated vehicles. Thus, to allow the debate to move beyond more binary discussions on privacy versus control/power and to a certain degree escape the shadow of the panopticon, we applied the Nissenbaum four theses framework of Contextual Integrity (CI). Her decision heuristic allowed us to introduce structure and a degree of precision in our thinking on the matter of privacy that represents a step forward to phenomena of privacy in a specific context. Our approach concludes that the VI-DAS 720-degree observation technology can respect the user's privacy through an appropriate flow of personal information. However, the flows of personal data must be strongly regulated to ensure that data is seen as a value in terms of a commodity to protect human life and not seen as an asset that needs to be turned into value in terms of capital or the facilitation of asymmetric power-relations.     

经典位置隐私保护算法分析与研究

在高速发展的信息时代,网络用户的隐私问题正受到普遍关注。而普试算法也正由于它能够为用户提供任何时间、任何地点、任何方式的方便服务使不同网络不同设备的人都得到满足而得到越来越多的应用。因此,我们在普适环境中,基于对位置隐私的分析,本文选取了包括Landmark Objects（LO）、False Dummies（FD）等在内的较为重要的不同位置隐私方法,并对其核心实现机制、特点以及优缺点等进行了分析。在此基础上,本文又对以上方法进行了比较并最终归纳总结了这些方法在面对多种形式攻击时的不足。     

A soft computing approach for privacy requirements engineering: The PriS framework

Soft computing continuously gains interest in many fields of academic and industrial domain; among the most notable characteristics for using soft computing methodological tools is the ability to handle with vague and imprecise data in decision making processes. Similar conditions are often encountered in requirements engineering. In this paper, we introduce the PriS approach, a security and privacy requirements engineering framework which aims at incorporating privacy requirements early in the system development process. Specifically, PriS provides a set of concepts for modelling privacy requirements in the organisation domain and a systematic way-of-working for translating these requirements into system models. The conceptual model of PriS uses a goal hierarchy structure. Every privacy requirement is either applied or not on every goal. To this end every privacy requirement is a variable that can take two values [0,1] on every goal meaning that the requirements constraints the goal (value 1) or not (value 0). Following this way of working PriS ends up suggesting a number of implementation techniques based on the privacy requirements constraining the respective goals. Taking into account that the mapping of privacy variables to a crisp set consisting of two values [0,1] is constraining, we extend also the PriS framework so as to be able to address the degree of participation of every privacy requirement towards achieving the generic goal of privacy. Therefore, we propose a fuzzification of privacy variables that maps the expression of the degree of participation of each privacy variable to the [0,1] interval. We also present a mathematical framework that allows the concurrent management of combined independent preferences towards the necessity of a privacy measure; among the advantages of the presented extended framework is the scalability of the approach in such a way that the results are not limited by the number of independent opinions or by the number of factors considered while reasoning for a specific selection of privacy measures.     

分布式数据挖掘的隐私保护问题

隐私保护是数据挖掘中一个重要的研究方向,如何在不违反隐私规定的情况下,利用数据挖掘工具发现有意义的知识是一个热点问题。本文介绍了分布式数据挖掘中隐私保护的现状,着重介绍分布式数据挖掘中隐私保护问题和技术。     

Irma Serrano-García: Award for Distinguished Contributions to Education and Training.

This document presents the citation awarded to Irma Serrano-García, recipient of the 2005 APA Award for Distinguished Contributions to Education and Training. A brief biography and a selected bibliography of Serrano-García accompany the citation. (PsycINFO Database Record (c) 2010 APA, all rights reserved)