Transactions on Data Privacy最新文献

Local synthesis for disclosure limitation that satisfies probabilistic k-anonymity criterion. 满足概率k-匿名准则的披露限制的局部综合。

IF 0.9

Transactions on Data Privacy Pub Date : 2017-04-01

Anna Oganian, Josep Domingo-Ferrer

{"title":"Local synthesis for disclosure limitation that satisfies probabilistic k-anonymity criterion.","authors":"Anna Oganian, Josep Domingo-Ferrer","doi":"","DOIUrl":"","url":null,"abstract":"Before releasing databases which contain sensitive information about individuals, data publishers must apply Statistical Disclosure Limitation (SDL) methods to them, in order to avoid disclosure of sensitive information on any identifiable data subject. SDL methods often consist of masking or synthesizing the original data records in such a way as to minimize the risk of disclosure of the sensitive information while providing data users with accurate information about the population of interest. In this paper we propose a new scheme for disclosure limitation, based on the idea of local synthesis of data. Our approach is predicated on model-based clustering. The proposed method satisfies the requirements of k-anonymity; in particular we use a variant of the k-anonymity privacy model, namely probabilistic k-anonymity, by incorporating constraints on cluster cardinality. Regarding data utility, for continuous attributes, we exactly preserve means and covariances of the original data, while approximately preserving higher-order moments and analyses on subdomains (defined by clusters and cluster combinations). For both continuous and categorical data, our experiments with medical data sets show that, from the point of view of data utility, local synthesis compares very favorably with other methods of disclosure limitation including the sequential regression approach for synthetic data generation.","PeriodicalId":44319,"journal":{"name":"Transactions on Data Privacy","volume":"10 1","pages":"61-81"},"PeriodicalIF":0.9,"publicationDate":"2017-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6760907/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144180249","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A model driven approach to data privacy verification in E-Health systems 电子医疗系统中数据隐私验证的模型驱动方法

IF 1.7

Transactions on Data Privacy Pub Date : 2015-12-01 DOI: 10.5555/2870503.2870506

AmatoFlora, MoscatoFrancesco

引用次数: 0

Attribute Based Group Key Management 基于属性的组密钥管理

IF 1.7

Transactions on Data Privacy Pub Date : 2014-12-01 DOI: 10.5555/2870614.2870619

NabeelMohamed, BertinoElisa

引用次数: 2

Preserving Differential Privacy in Degree-Correlation based Graph Generation. 基于度相关图生成的差分隐私保护。

IF 1.7

Transactions on Data Privacy Pub Date : 2013-08-01

Yue Wang, Xintao Wu

{"title":"Preserving Differential Privacy in Degree-Correlation based Graph Generation.","authors":"Yue Wang, Xintao Wu","doi":"","DOIUrl":"","url":null,"abstract":"Enabling accurate analysis of social network data while preserving differential privacy has been challenging since graph features such as cluster coefficient often have high sensitivity, which is different from traditional aggregate functions (e.g., count and sum) on tabular data. In this paper, we study the problem of enforcing edge differential privacy in graph generation. The idea is to enforce differential privacy on graph model parameters learned from the original network and then generate the graphs for releasing using the graph model with the private parameters. In particular, we develop a differential privacy preserving graph generator based on the dK-graph generation model. We first derive from the original graph various parameters (i.e., degree correlations) used in the dK-graph model, then enforce edge differential privacy on the learned parameters, and finally use the dK-graph model with the perturbed parameters to generate graphs. For the 2K-graph model, we enforce the edge differential privacy by calibrating noise based on the smooth sensitivity, rather than the global sensitivity. By doing this, we achieve the strict differential privacy guarantee with smaller magnitude noise. We conduct experiments on four real networks and compare the performance of our private dK-graph models with the stochastic Kronecker graph generation model in terms of utility and privacy tradeoff. Empirical evaluations show the developed private dK-graph generation models significantly outperform the approach based on the stochastic Kronecker generation model.","PeriodicalId":44319,"journal":{"name":"Transactions on Data Privacy","volume":"6 2","pages":"127-145"},"PeriodicalIF":1.7,"publicationDate":"2013-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3979555/pdf/nihms-555508.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"32256559","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Preserving Differential Privacy in Degree-Correlation based Graph Generation 基于度相关图生成的差分隐私保护

IF 1.7

Transactions on Data Privacy Pub Date : 2013-08-01 DOI: 10.5555/2612167.2612168

WangYue, WUXin-Tao

引用次数: 15

Practicing Differential Privacy in Health Care 在医疗保健中实行差别隐私

IF 1.7

Transactions on Data Privacy Pub Date : 2013-04-01 DOI: 10.5555/2612156.2612159

K. DankarFida, El EmamKhaled

引用次数: 3

Differential-Private Data Publishing Through Component Analysis. 通过组件分析的差分私有数据发布。