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Threat Modeling Gameplay with EoP

You're reading from   Threat Modeling Gameplay with EoP A reference manual for spotting threats in software architecture

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Product type Paperback
Published in Aug 2024
Publisher Packt
ISBN-13 9781804618974
Length 256 pages
Edition 1st Edition
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Author (1):
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Brett Crawley Brett Crawley
Author Profile Icon Brett Crawley
Brett Crawley
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Toc

Table of Contents (18) Chapters Close

Preface 1. Chapter 1: Game Play 2. Chapter 2: Spoofing FREE CHAPTER 3. Chapter 3: Tampering 4. Chapter 4: Repudiation 5. Chapter 5: Information Disclosure 6. Chapter 6: Denial of Service 7. Chapter 7: Elevation of Privilege 8. Chapter 8: Privacy 9. Chapter 9: Transfer 10. Chapter 10: Retention/Removal 11. Chapter 11: Inference 12. Chapter 12: Minimization 13. Glossary
14. Further Reading 15. Licenses for third party content 16. Index 17. Other Books You May Enjoy

10. of Inference

You do not make any checks on personal data before you use it for training machine learning models.

Threat

image

You are not checking personal data before using it in training, so you cannot expect your model to be accurate. Therefore, some of the decisions it will make will also not be accurate, which implies it may unfairly make decisions about a subject, such as mortgage approvals/refusals. A requirement of many data privacy regulations is that the data be kept accurate. It is also possible that an attacker may modify the data deliberately to poison the model and you could be leaving yourself open to injection attacks.

GDPR

Chapter 2, Art. 5 – 1.(d)

Chapter 2, Art. 5 – 1.(f)

CCPA & CPRA

N/A

OECD

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