Privacy Threat Modeling Analysis of Cloud Services Against Privacy Regulations
summary
Claims that regulation-driven privacy threat modeling for cloud services produces actionable requirements and LINDDUN and CPTM and Q-TTM methods map to concrete threats, while a built privacy tagging layer converts GDPR articles into user stories and features. Demonstrates parameterization (eg breach notification = 72 hours, access response = 30 vs 15 days) and proposes reusable, pre-populated threat lists with applicability rules for interactions; framework is prototyped on a sample cloud app and reported as repeatable and scalable.
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chapters (14)
- 0:00Start
- 0:57Privacy Modeling for Cloud Services Based on the Compliance Regulations
- 2:02What Is Threat Modeling
- 2:25What Does It Mean for Privacy
- 4:16Goal Opportunity Statements
- 5:08Privacy Threat Modeling
- 8:52Identifiability
- 9:12Detectability
- 17:48Pros and Cons of Cptm
- 20:28Data Flow Diagram
- 26:17Examples
- 34:47Lack of Data Protection
- 35:26Parameterizing Threats
- 35:53Parametrize the Threats
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