Sentence examples for data privacy models from inspiring English sources

His research focuses on the construction and evaluation of data privacy models for personal information that is collected, stored, and shared in large complex systems.

Rather than determining the specific transformation that should be carried out on the original data, privacy models specify conditions that the data set must satisfy to keep disclosure risk under control.

But the future of commercial data privacy models is also getting attention.

As important, we are developing a strict data privacy model for cross-institutional data sharing that we believe can be adopted by other organizations.

Or perhaps our real governments will step forward and follow the EU data privacy model to halt Facebook's practices.

Thus, to be useful for big data, a privacy model must allow anonymized data to stay linkable to some extent.

To build a secure and efficient cloud computing environment, we extend and improve the existing CSP and TPA security models by leveraging the properties into a single triangular data privacy-preserving model to provide the auditing capability to all the key stakeholders.

Composability can be evaluated between data releases that satisfy the same privacy model, between data releases that satisfy different privacy models and between a data release that satisfies a privacy model and non-anonymized data.

Sorias-Comas and DomingoFerrer in "Big Data Privacy: Challenges to Privacy Principles and Models," after a comprehensive overview of data privacy techniques, discuss the challenges that need to be addressed in order to apply and/or extend such techniques to address key challenges in big data privacy, namely composability, linkability, and low computational costs.

At DIMACS, we have working groups on a wide variety of epidemiology problems, such as vaccination strategies, adverse event detection, health data privacy and confidentiality, and models of social disruptions caused by bioterrorist events.

This volume covers a range of aspects, including: big data management for healthcare; physiological and gut microbiota – data collection and analysis; big data standardization and ontology; and personal data privacy and systems level modeling in the healthcare context.