Transactions on Data Privacy最新文献

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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
{"title":"A model driven approach to data privacy verification in E-Health systems","authors":"AmatoFlora, MoscatoFrancesco","doi":"10.5555/2870503.2870506","DOIUrl":"https://doi.org/10.5555/2870503.2870506","url":null,"abstract":"Last years experienced the growth of new technologies able to remotely monitor health state of persons. This includes both (even complex) Medical devices and all kind of wearable device. In additio...","PeriodicalId":44319,"journal":{"name":"Transactions on Data Privacy","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2015-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71137011","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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
{"title":"Attribute Based Group Key Management","authors":"NabeelMohamed, BertinoElisa","doi":"10.5555/2870614.2870619","DOIUrl":"https://doi.org/10.5555/2870614.2870619","url":null,"abstract":"Attribute based systems enable fine-grained access control among a group of users each identified by a set of attributes. Secure collaborative applications need such flexible attribute based system...","PeriodicalId":44319,"journal":{"name":"Transactions on Data Privacy","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71136872","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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,&nbsp;Xintao Wu","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":44319,"journal":{"name":"Transactions on Data Privacy","volume":null,"pages":null},"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
{"title":"Preserving Differential Privacy in Degree-Correlation based Graph Generation","authors":"WangYue, WUXin-Tao","doi":"10.5555/2612167.2612168","DOIUrl":"https://doi.org/10.5555/2612167.2612168","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 d...","PeriodicalId":44319,"journal":{"name":"Transactions on Data Privacy","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2013-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71130902","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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
{"title":"Practicing Differential Privacy in Health Care","authors":"K. DankarFida, El EmamKhaled","doi":"10.5555/2612156.2612159","DOIUrl":"https://doi.org/10.5555/2612156.2612159","url":null,"abstract":"Differential privacy has gained a lot of attention in recent years as a general model for the protection of personal information when used and disclosed for secondary purposes. It has also been pro...","PeriodicalId":44319,"journal":{"name":"Transactions on Data Privacy","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2013-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71130195","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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。
IF 1.7
Transactions on Data Privacy Pub Date : 2013-04-01
Xiaoqian Jiang, Zhanglong Ji, Shuang Wang, Noman Mohammed, Samuel Cheng, Lucila Ohno-Machado
{"title":"Differential-Private Data Publishing Through Component Analysis.","authors":"Xiaoqian Jiang,&nbsp;Zhanglong Ji,&nbsp;Shuang Wang,&nbsp;Noman Mohammed,&nbsp;Samuel Cheng,&nbsp;Lucila Ohno-Machado","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>A reasonable compromise of privacy and utility exists at an \"appropriate\" resolution of the data. We proposed novel mechanisms to achieve privacy preserving data publishing (PPDP) satisfying ε-<i>differential privacy</i> with improved utility through <i>component analysis</i>. The mechanisms studied in this article are Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA). The differential PCA-based PPDP serves as a general-purpose data dissemination tool that guarantees better utility (i.e., smaller error) compared to Laplacian and Exponential mechanisms using the same \"privacy budget\". Our second mechanism, the differential LDA-based PPDP, favors data dissemination for classification purposes. Both mechanisms were compared with state-of-the-art methods to show performance differences.</p>","PeriodicalId":44319,"journal":{"name":"Transactions on Data Privacy","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2013-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3883117/pdf/nihms456798.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"32017146","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
The PROBE Framework for the Personalized Cloaking of Private Locations 用于私人位置个性化伪装的PROBE框架
IF 1.7
Transactions on Data Privacy Pub Date : 2010-08-01 DOI: 10.5555/1824401.1824404
D. Luisa, BertinoElisa, SilvestriClaudio
{"title":"The PROBE Framework for the Personalized Cloaking of Private Locations","authors":"D. Luisa, BertinoElisa, SilvestriClaudio","doi":"10.5555/1824401.1824404","DOIUrl":"https://doi.org/10.5555/1824401.1824404","url":null,"abstract":"The widespread adoption of location-based services (LBS) raises increasing concerns for the protection of personal location information. A common strategy, referred to as obfuscation (or cloaking),...","PeriodicalId":44319,"journal":{"name":"Transactions on Data Privacy","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2010-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71123236","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Beyond k-Anonymity: A Decision Theoretic Framework for Assessing Privacy Risk 超越k-匿名:一个评估隐私风险的决策理论框架
IF 1.7
Transactions on Data Privacy Pub Date : 2006-12-13 DOI: 10.1007/11930242_19
Guy Lebanon, M. Scannapieco, Mohamed R. Fouad, E. Bertino
{"title":"Beyond k-Anonymity: A Decision Theoretic Framework for Assessing Privacy Risk","authors":"Guy Lebanon, M. Scannapieco, Mohamed R. Fouad, E. Bertino","doi":"10.1007/11930242_19","DOIUrl":"https://doi.org/10.1007/11930242_19","url":null,"abstract":"","PeriodicalId":44319,"journal":{"name":"Transactions on Data Privacy","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2006-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74210167","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 23
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