共查询到19条相似文献,搜索用时 109 毫秒
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推荐系统的建立依赖用户的个人隐私信息,攻击者可以通过推荐的结果对用户的状态和行为进行预测.目前,虽然有对基于协同过滤近邻隐私保护的研究,但是对基于模型的隐私保护的关注度并不够高.差分隐私理论定义了一个相当严格的防攻击模型,通过添加噪声使数据失真达到隐私保护的目的,而且用户的兴趣存在兴趣漂移问题,对推荐效果造成影响,因此,提出基于差分隐私理论和时序理论构建基于模型的推荐系统.首先,根据差分隐私理论,给用户的评分数据增加小波动的符合Laplace分布的噪声,增大待分解矩阵的安全系数;然后,在随机梯度下降模型的基础上,将时序因子建模为时间权重,提高模型的准确性.实验证明该算法的准确性,并且为增强隐私研究提供了新的思路. 相似文献
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位置轨迹大数据的安全分享、发布需求离不开位置轨迹隐私保护技术支持。在差分隐私出现之前,K-匿名及其衍生模型为位置轨迹隐私保护提供了一种量化评估的手段,但其安全性严重依赖于攻击者所掌握的背景知识,当有新的攻击出现时模型无法提供完善的隐私保护。差分隐私技术的出现有效地弥补了上述问题,越来越多地应用于轨迹数据隐私发布领域中。该文对基于差分隐私理论的轨迹隐私保护技术进行了研究与分析,重点介绍了差分隐私模型下位置直方图、轨迹直方图等空间统计数据发布方法,差分隐私模型下轨迹数据集发布方法,以及连续轨迹实时发布隐私保护模型。与此同时,在对现有方法对比分析的基础上,提出了未来的重点发展方向。 相似文献
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为了解决基于位置的服务(Location Based Service,LBS)在收集用户位置数据时造成的隐私泄露,提出一种本地化差分隐私位置发布模型.首先,该模型采用了灵活的位置隐私保护方案(个性化隐私设置),即由用户选择已设定的多种隐私策略或定制隐私策略,在此基础上设计了定制隐私策略位置扰动算法(Customized Privacy policy Location Perturbation algorithm,CPLP);其次,提出并设计一种基于隐马尔可夫模型的时序关联位置隐私发布算法(Temporal Relational Location Privacy publishing algorithm,TRLP),解决发布时序位置时产生的隐私泄露;最后,在GeoLife数据集和Gowalla数据集上通过对比实验验证了该模型的有效性. 相似文献
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《电子技术与软件工程》2017,(24)
信息技术的不断发展使得医疗行业的信息化进程不断加速深入。网络中信息的爆炸式增长同样出现在医疗领域,使之与互联网数据一同迈入大数据时代。如何在利用医疗大数据的同时保护好个人的隐私安全具有重要意义。医疗大数据在内容以及隐私层面均比网络大数据更加复杂更加敏感,传统匿名技术的缺陷是过度依赖背景知识的假设,而差分隐私的定义则相对显得更加完善与理想。通过差分隐私在医疗大数据的应用,探讨分析医疗大数据在实践差分隐私保护时需要面对的挑战与问题。 相似文献
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基于匿名化技术的理论基础,采用DBSCAN聚类算法对数据记录进行聚类,实现将个体记录匿名化隐藏于一组记录中。为提高隐私保护程度,对匿名化划分的数据添加拉普拉斯噪声,扰动个体数据真实值,以实现差分隐私保护模型的要求。通过聚类,分化查询函数敏感性,提高数据可用性。对算法隐私性进行证明,并实验说明发布数据的可用性。 相似文献
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相对于ε-本地差分隐私(LDP)机制,(ε, δ)-本地差分隐私模型下的方案具有更小的误差边界和更高的数据效用。然而,当前的(ε, δ)-本地差分隐私均值估计机制仍存在估计误差大、数据效用低等问题。因此,针对均值估计问题,该文提出两种新的(ε, δ)-本地差分隐私均值估计机制:基于区间的均值估计机制(IM)和基于近邻的均值估计机制(NM)。IM的主要思想是:划分扰动后的数据到3个区间,真实数据以较大概率扰动到中间的区间,以较小概率扰动到两边的区间,收集者直接对扰动数据求均值得到无偏估计。NM的主要思想是:把真实数据以较大概率扰动到其邻域,以较小概率扰动到距离较远的值,收集者结合期望最大化算法得到高准确度的估计均值。最后,该文通过理论分析证明了IM和NM均可以满足隐私保护要求,并通过实验证实了IM和NM的数据效用优于现有机制。 相似文献
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云计算的出现为多个数据拥有者进行数据集成发布及协同数据挖掘提供了更广阔的平台,在数据即服务模式(Daa S,data as a service)下,集成数据被部署在非完全可信的服务运营商平台上,数据隐私保护成为制约该模式应用和推广的挑战性问题。为防止数据集成时的隐私泄露,提出一种面向Daa S应用的两级隐私保护机制。该隐私保护机制独立于具体的应用,将数据属性切分到不同的数据分块中,并通过混淆数据确保数据在各个分块中均衡分布,实现对数据集成隐私保护。通过分析证明该隐私保护机制的合理性,并通过实验验证该隐私保护机制具有较低的计算开销。 相似文献
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面向频繁模式挖掘的差分隐私保护研究综述 总被引:1,自引:0,他引:1
频繁模式挖掘是数据挖掘的一个基本问题,其模式本身和相应计数都有可能泄露隐私信息。当前,差分隐私通过添加噪音使数据失真,有效实现了隐私保护的目的。首先介绍了差分隐私保护模型的理论基础;其次,详细综述了差分隐私下3种典型的频繁模式挖掘方法的最新研究进展,并进行对比性分析;最后对未来的研究方向进行了展望。 相似文献
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The privacy protection in big data is a research hotspot in the field of cyberspace security.As a strict and provable definition of privacy protection,studying application status of differential privacy protection in big data environment can provide reference and guidance for its subsequent system applications.Based on the analysis of the related concepts and technical characteristics of differential privacy protection,the application of differential privacy protection technology was reviewed in data distribution and analysis,cloud computing and big data computing,location and trajectory services and social networks,which expounded the current representative research results and analyzed its existing problems.The research shows that the existing results have made effective innovation and exploration of differential privacy protection applications from the aspects of differential privacy protection mechanism,noise addition mechanism and location,and data processing methods,and the related results have been cross-applied in different scenarios.Finally,four major problems that need to be studied in the further systematic application of differential privacy protection in the big data environment are proposed. 相似文献
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Aiming at dealing with prospect knowledge and complex combinatorial attack,a new location big data publishing mechanism under differential privacy technology was given.And innovative usability evaluation feedback mechanism was designed.It gave corresponding solution details for the sensitive attributes and the identity recognition to analyze the quality of service,aimed at privacy protecting for location based big data under situations like combination of location information and non-location information and attacker’s arbitrary background knowledge.Simulation results based on different spatial indexing technology proved that the new publishing model has a higher accuracy under specified privacy conditions for the location query service. 相似文献
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With the arrival of the era of big data sharing,data privacy protection issues will be highlighted.Since its introduction in 2006,differential privacy technology has been widely researched in data mining and data publishing.In recent years,Google,Apple and other companies have introduced differential privacy technology into the latest products,and differential privacy technology has become the focus of academia and industry again.Firstly,the traditional centralized model of differential privacy was summarized,from the perspective of analysis of data mining and data released in the differential privacy way.Then the latest local differential privacy regarding data collection and data analysis based on the local model was described,involving crowdsourcing with random response technology,BloomFilter,statistical inference techniques.Finally,the main problems and solutions of differential privacy technology were summarized. 相似文献
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For the study of privacy-utility trade-off in local differential privacy,the utility optimization models of binary generalized random response mechanism for the case of differential privacy and approximate differential privacy were established.By graphic method,optimality proof,software solution and extreme point method,the exact expression of the optimal utility with privacy budget and the distribution of input data was obtained,and the corresponding optimal randomized response mechanism was given.The results show that both the optimal utility and optimal mechanism are related to privacy budget and input data distribution.Moreover,the discussion for multivariate randomized response mechanism shows that the method of extreme points of local differential privacy is feasible to the solution. 相似文献
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机器学习的飞速发展使其成为数据挖掘领域最有效的工具之一,但算法的训练过程往往需要大量的用户数据,给用户带来了极大的隐私泄漏风险.由于数据统计特征的复杂性及语义丰富性,传统隐私数据发布方法往往需要对原始数据进行过度清洗,导致数据可用性低而难以再适用于数据挖掘任务.为此,提出了一种基于生成对抗网络(Generative Adversarial Network,GAN)的差分隐私数据发布方法,通过在GAN模型训练的梯度上添加精心设计的噪声来实现差分隐私,确保GAN可无限量生成符合源数据统计特性且不泄露隐私的合成数据.针对现有同类方法合成数据质量低、模型收敛缓慢等问题,设计多种优化策略来灵活调整隐私预算分配并减小总体噪声规模,同时从理论上证明了合成数据严格满足差分隐私特性.在公开数据集上与现有方法进行实验对比,结果表明本方法能够更高效地生成质量更高的隐私保护数据,适用于多种数据分析任务. 相似文献
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To address the issues of privacy budget and quality of service in trajectory differential privacy protection,a trajectory differential privacy mechanism integrating prediction disturbance was proposed.Firstly,Markov chain and exponential perturbation method were used to predict the location which satisfies the differential privacy and temporal and spatial security,and service similarity map was introduced to detect the availability of the location.If the prediction was successful,the prediction location was directly used to replace the location of differential disturbance,to reduce the privacy cost of continuous query and improve the quality of service.Based on this,the trajectory privacy budget allocation mechanism based on w sliding window was designed to ensure that any continuous w queries in the trajectory meet the ε-differential privacy and solve the trajectory privacy problem of continuous queries.In addition,a privacy customization strategy was designed based on the sensitivity map.By customizing the privacy sensitivity of semantic location,the privacy budget could be customized to improve its utilization.Finally,the validity of the scheme was verified by real data set experiment.The results illustrate that it offers the better privacy and quality of service. 相似文献