PRIVACY BY DESIGN
-Paper on developers' lack of privacy education: https://arxiv.org/ftp/arxiv/papers/1805/1805.09485.pdf
-The Privacy Engineer's Manifesto: Getting from Policy to Code to QA to Value by Dennedy, Fox, and Finneran: https://www.amazon.com/Privacy-Engineers-Manifesto-Getting-Policy/dp/1430263555
OWASP Top Ten for Privacy
-Main project: https://www.owasp.org/index.php/OWASP_Top_10_Privacy_Risks_Project#tab=Main
-PDF of Top Ten: https://www.owasp.org/images/0/0a/OWASP_Top_10_Privacy_Countermeasures_v1.0.pdf
Privacy Impact Assessment
-Simple PIA how-to guide: https://www.privacy.org.nz/assets/Files/Guidance/Privacy-Impact-Assessment-Part-2-FA.pdf
-CNIL PIA knowledgebase: https://www.cnil.fr/en/privacy-impact-assessment-pia
-PIA template: https://ico.org.uk/media/1042836/pia-code-of-practice-editable-annexes.docx
-Automated PIA tool: https://iapp.org/resources/apia/
-PIA academic study: https://iapp.org/media/pdf/knowledge_center/Making_PIA__more_effective.pdf
Data Minimization
-Minimizing API personal data: https://www.w3.org/2001/tag/doc/APIMinimization
Default Settings
-Examples of intrusive default settings: https://www.washingtonpost.com/news/the-switch/wp/2018/06/01/hands-off-my-data-15-default-privacy-settings-you-should-change-right-now/?noredirect=on&utm_term=.7fff10dcce21
-Firefox default settings for privacy overview: https://blog.mozilla.org/blog/2019/06/04/when-it-comes-to-privacy-default-settings-matter/
PRIVACY TECHNOLOGIES
Encryption
-OWASP Guide to Cryptography: https://www.owasp.org/index.php/Guide_to_Cryptography
-FIPS 140-2 Security Requirements for Cryptographic Modules: https://nvlpubs.nist.gov/nistpubs/FIPS/NIST.FIPS.140-2.pdf
Differential Privacy
-"Privacy-Preserving Data Publishing: A Survey of Recent Developments": https://www.cs.sfu.ca/~wangk/pub/FWCY10csur.pdf
-TensorFlow Privacy GitHub and tutorials: https://github.com/tensorflow/privacy/blob/master/tutorials/walkthrough/walkthrough.md
-RAPPOR: Randomized Aggregatable Privacy-Preserving Ordinal Response: https://ai.google/research/pubs/pub42852
-Harvard Privacy Tools: https://privacytools.seas.harvard.edu/courses-educational-materials
Privacy Preserving Ad Click Attribution
-Overview by the developer: https://webkit.org/blog/8943/privacy-preserving-ad-click-attribution-for-the-web/
-GitHub repository: https://github.com/WICG/ad-click-attribution
-Brave browser Basic Attention Token: https://basicattentiontoken.org/
Federated Learning
-Federated Learning for Mobile Keyboard Prediction: https://arxiv.org/pdf/1811.03604.pdf
-Comic overview of federated learning: https://federated.withgoogle.com/
-TensorFlow Federated: https://www.tensorflow.org/federated
-PySyft Library: https://github.com/OpenMined/PySyft/
-PyTorch + PySyft tutorial: https://blog.openmined.org/upgrade-to-federated-learning-in-10-lines/
-Udacity course on federated learning: https://www.udacity.com/course/secure-and-private-ai--ud185
Homomorphic Encryption
-Google's open source Privacy Join and Compute: https://github.com/google/private-join-and-compute
-Technical paper on the Privacy Join and Compute research: https://eprint.iacr.org/2019/723
-Google blog overview of Privacy Join and Compute: https://security.googleblog.com/2019/06/helping-organizations-do-more-without-collecting-more-data.html