共查询到20条相似文献,搜索用时 796 毫秒
1.
2.
基于聚类的高效(K,L)-匿名隐私保护 总被引:1,自引:0,他引:1
为防止发布数据中敏感信息泄露,提出一种基于聚类的匿名保护算法.分析易被忽略的准标识符对敏感属性的影响,利用改进的K-means聚类算法对数据进行敏感属性聚类,使类内数据更相似.考虑等价类内敏感属性的多样性,对待发布表使用(K,L)-匿名算法进行聚类.实验结果表明,与传统K-匿名算法相比,该算法在实现隐私保护的同时,数据信息损失较少,执行时间较短. 相似文献
3.
对数据发布中传统方法脱敏多元组关系-集值数据可能导致信息泄露以及产生较高信息损失的问题进行研究,提出基于(K,L)-多样性模型的多元组关系-集值数据的脱敏方法PAHI.根据准标识符将多元组数据转换为单元组数据;用信息增益比优化分割方法,实现集值数据K-匿名;引入敏感度值建立集值指纹桶,采用敏感度距离优化剩余元组的处理,... 相似文献
4.
5.
提出了一种新的K-匿名模型对隐私信息进行保护,将熵分类的方法应用于K-匿名模型上,实验表明该模型的有效性,利用该模型对数据进行K-匿名处理后,确保共享数据具有很高的精确度,尽可能接近原始数据,同时有效地防止隐私信息的泄露。 相似文献
6.
针对快递单号被盗取和快递单信息保护不当造成的隐私泄露问题进行了研究, 提出了一种新型K-匿名模型对快递信息进行匿名处理。该方法通过随机打破记录中属性值之间的关系来匿名数据, 相比于其他传统方法, 克服了数据间统计关系丢失的问题和先验知识攻击。实验结果表明, 新型K-匿名方法能够加强隐私保护和提高知识保护的准确性。 相似文献
7.
建模是不确定性数据管理的基础,K-匿名隐私保护模型中不确定性数据有其特殊性:它是人为泛化后的不确定性数据,泛化后的每个实例还原成泛化前元组的概率是相等的。由于其特殊性,以往针对非人为造成不确定性的数据建模方法已经不能简单地用于描述K-匿名隐私保护模型中不确定性数据。为了描述K-匿名隐私保护模型中不确定性数据,本文提出几种针对它的新建模方法:Kattr模型使用attrib-ute-ors方法来描述K-匿名数据中准标识符属性值的不确定性;Ktuple模型把K-匿名表不确定属性值看成是一个关系值,对关系值使用tuple-ors方法来描述;Kupperlower模型把K-匿名表泛化值范围分开成两个字段:上限和下限;Ktree模型根据K-匿名表是对普通表通过泛化树泛化而形成这一特性逆向拆分成树形结构。由这几种模型及它们之间的组合构成了一个描述K-匿名隐私保护模型中不确定性数据的模型空间。并且,本文讨论了模型空间里各种模型的完备性和封闭性等性质。 相似文献
8.
9.
(p,a)-sensitive k-匿名隐私保护模型 总被引:1,自引:0,他引:1
提出了一种(p,a)-sensitive k-匿名模型,将敏感属性根据敏感度进行分组,然后给各分组设置不同的约束,并给出了(p,a)-sensitive K-匿名算法.实验结果表明该方法可以明显地减少隐私泄露,增强了数据发布的安全性. 相似文献
10.
在K-匿名模型的基础上提出了(s,d)-个性化K-匿名隐私保护模型,该模型能很好地解决属性泄漏问题,并通过实验证明了该模型的可行性。 相似文献
11.
12.
视图发布给数据交换带来了方便,但也带来了安全隐患,在视图发布过程中有可能造成信息的泄漏.因此,保证发布视图的安全成为数据库安全的一个新课题.理论上讲,防止视图发布过程中信息泄漏的方法可分为两种:一种是针对视图接受者,另一种是针对视图发布者.在实际应用中,第1种方法是很难实现的,因此,人们把研究重点都放在第2种方法上.到目前为止,人们提出了有关的评估算法和保护模型,但是它们都不能够从根本上解决问题.为了消除信息泄漏,提出了相对误差的信息泄漏测量方法,并给出了相应的算法,在此基础上,给出了一个基于关键元组的信息泄漏消除算法,并用实验证明该算法能够有效地消除信息泄漏,保证视图的安全. 相似文献
13.
Protecting respondents identities in microdata release 总被引:26,自引:0,他引:26
Today's globally networked society places great demands on the dissemination and sharing of information. While in the past released information was mostly in tabular and statistical form, many situations call for the release of specific data (microdata). In order to protect the anonymity of the entities (called respondents) to which information refers, data holders often remove or encrypt explicit identifiers such as names, addresses, and phone numbers. Deidentifying data, however, provides no guarantee of anonymity. Released information often contains other data, such as race, birth date, sex, and ZIP code, that can be linked to publicly available information to reidentify respondents and inferring information that was not intended for disclosure. In this paper we address the problem of releasing microdata while safeguarding the anonymity of respondents to which the data refer. The approach is based on the definition of k-anonymity. A table provides k-anonymity if attempts to link explicitly identifying information to its content map the information to at least k entities. We illustrate how k-anonymity can be provided without compromising the integrity (or truthfulness) of the information released by using generalization and suppression techniques. We introduce the concept of minimal generalization that captures the property of the release process not distorting the data more than needed to achieve k-anonymity, and present an algorithm for the computation of such a generalization. We also discuss possible preference policies to choose among different minimal generalizations 相似文献
14.
Anonymity preserving pattern discovery 总被引:5,自引:0,他引:5
Maurizio Atzori Francesco Bonchi Fosca Giannotti Dino Pedreschi 《The VLDB Journal The International Journal on Very Large Data Bases》2008,17(4):703-727
It is generally believed that data mining results do not violate the anonymity of the individuals recorded in the source database. In fact, data mining models and patterns, in order to ensure a required
statistical significance, represent a large number of individuals and thus conceal individual identities: this is the case
of the minimum support threshold in frequent pattern mining. In this paper we show that this belief is ill-founded. By shifting the concept of k
-anonymity from the source data to the extracted patterns, we formally characterize the notion of a threat to anonymity in the context
of pattern discovery, and provide a methodology to efficiently and effectively identify all such possible threats that arise
from the disclosure of the set of extracted patterns. On this basis, we obtain a formal notion of privacy protection that
allows the disclosure of the extracted knowledge while protecting the anonymity of the individuals in the source database.
Moreover, in order to handle the cases where the threats to anonymity cannot be avoided, we study how to eliminate such threats
by means of pattern (not data!) distortion performed in a controlled way. 相似文献
15.
隐私保护已经成为区块链技术真正从理论到现实应用必须解决的关键问题。实际应用中存在一种按需披露的隐私保护需求,受组播安全通信机制的启发,提出一种按需披露的区块链隐私保护机制(PPM-ODB,privacy protection mechanism of on-demand disclosure on blockchain)。该机制通过改进基于RSA的匿名多接收者加密方案来实现隐私信息一对多的加解密、知情者的匿名性保护和隐私泄露的可追溯,通过采用Quorum链隐私保护机制来实现密钥在隐私信息拥有者和知情者间的安全高效分发。实验证明了PPM-ODB机制可保证隐私数据的保密性,及其在时间和存储开销上的优越性,并建议知情者的个数少于100,以获得良好的用户体验。 相似文献
16.
Erin E. Hollenbaugh Marcia K. Everett 《Journal of Computer-Mediated Communication》2013,18(3):283-302
The connections between anonymity and self‐disclosure online have received research attention, but the results have been inconclusive with regard to self‐disclosure in blogs. This quantitative content analysis of 154 personal journal blogs tested some assumptions of the online disinhibition effect in order to examine the effect of types of anonymity on the amount, breadth, and depth of self‐disclosure in blog entries. Results showed that participants disclosed more information in their blog entries when they were more visually identified (sharing a picture of themselves), contrary to the assumptions of the online disinhibition effect. Overall, a trend emerged where visual anonymity led to less disclosiveness, and discursive anonymity (sharing one's real name) led to less disclosiveness for particular types of bloggers. 相似文献
17.
18.
一种基于聚类的数据匿名方法 总被引:10,自引:0,他引:10
为了防止个人隐私的泄漏,在数据共享前需要对其在准标识符上的属性值作数据概化处理,以消除链接攻击,实现在共享中对敏感属性的匿名保护.概化处理增加了属性值的不确定性,不可避免地会造成一定的信息损失.传统的数据概化处理大都建立在预先定义的概念层次结构的基础上,会造成过度概化,带来许多不必要的信息损失.将准标识符中的属性分为有序属性和无序属性两种类型,分别给出了更为灵活的相应数据概化策略.同时,通过考察数据概化前后属性值不确定性程度的变化,量化地定义了数据概化带来的信息损失.在此基础上,将数据匿名问题转化为带特定约束的聚类问题.针对l-多样模型,提出了一种基于聚类的数据匿名方法L-clustering.该方法能够满足在数据共享中对敏感属性的匿名保护需求,同时能够很好地降低实现匿名保护时概化处理所带来的信息损失. 相似文献
19.
20.