| Title | Policy-Based De-Identification Test Framework |
| Publication Type | Conference Paper |
| Year of Publication | 2019 |
| Authors | Gerl, Armin, Becher, Stefan |
| Conference Name | 2019 IEEE World Congress on Services (SERVICES) |
| Date Published | jul |
| Keywords | Big Data, Data models, data privacy, data protection, data-centered society, Distributed Information Systems, domain specific language, domain-specific language, General Data Protection Regulation, Generators, Human Behavior, Load modeling, performance evaluation, personal privacy anonymization, policy-based de-identification test framework, privacy, privacy models, Privacy Policies, privacy preserving, privacy protection, pseudonymization, pubcrawl, Scalability |
| Abstract | Protecting privacy of individuals is a basic right, which has to be considered in our data-centered society in which new technologies emerge rapidly. To preserve the privacy of individuals de-identifying technologies have been developed including pseudonymization, personal privacy anonymization, and privacy models. Each having several variations with different properties and contexts which poses the challenge for the proper selection and application of de-identification methods. We tackle this challenge proposing a policy-based de-identification test framework for a systematic approach to experimenting and evaluation of various combinations of methods and their interplay. Evaluation of the experimental results regarding performance and utility is considered within the framework. We propose a domain-specific language, expressing the required complex configuration options, including data-set, policy generator, and various de-identification methods. |
| DOI | 10.1109/SERVICES.2019.00101 |
| Citation Key | gerl_policy-based_2019 |
Policy-Based De-Identification Test Framework