Titlebook: Data Privacy Management and Security Assurance; 11th International W Giovanni Livraga,Vicen? Torra,Neeraj Suri Conference proceedings 2016

JAR

書目名稱Data Privacy Management and Security Assurance影響因子(影響力)




書目名稱Data Privacy Management and Security Assurance影響因子(影響力)學科排名




書目名稱Data Privacy Management and Security Assurance網(wǎng)絡公開度




書目名稱Data Privacy Management and Security Assurance網(wǎng)絡公開度學科排名




書目名稱Data Privacy Management and Security Assurance被引頻次




書目名稱Data Privacy Management and Security Assurance被引頻次學科排名




書目名稱Data Privacy Management and Security Assurance年度引用




書目名稱Data Privacy Management and Security Assurance年度引用學科排名




書目名稱Data Privacy Management and Security Assurance讀者反饋




書目名稱Data Privacy Management and Security Assurance讀者反饋學科排名

Ingratiate

0302-9743 nt, DPM 2016 and the 5th International Workshop on Quantitative Aspects in Security Assurance, QASA 2016, held in Heraklion, Crete, Greece, in September 2016.?9 full papers and 4 short papers out of 24 submissions are included in the DPM 2016 Workshop. They are organized around areas related? to the

肥料

The Development of Conversational Behaviorht to a secure and private processing of their personal data. We define eight quality metrics for transparency and we demonstrate the usage and the effectiveness of the metrics by assessing transparency on the Microsoft HealthVault, an on-line platform for users to collect, store, and share medical records.

猛然一拉

Louis A. Gottschalk,Regina L. Ulianaacker. To this end, we model attacks as an interaction between an attacker and a defender and infer the two types of behavior observed based on justifiable assumptions on key variables such as detection thresholds. On the basis of our analysis, it follows that bi-modal detection capabilities are optimal.

Limousine

我不重要

我不重要

令人作嘔

ENACT

habile

Isabelle Modelchecking for Insider Threatsbelle. Isabelle can simply accommodate classical CTL type Modelchecking. We integrate CTL Modelchecking into the Isabelle Insider framework. A running example of an IoT attack on privacy motivates the method throughout and illustrates how the enhanced framework fully supports realistic modeling and analysis of IoT Insiders.