Privacy Enhancing Technologies

Intro + FHE (1h)
Lecture Notes, Class Notes

• Refresher on complexity theory, cryptography, and probability theory which will be helpful for the course.
• “The Moral Character of Cryptographic Work” [Rogaway, 2015]
• Craig Gentry’s nice intro to FHE

Commitment schemes + PRFs (3h)
Lecture Notes, Class Notes

• Boneh-Shoup (§3.12, §8.10.2)
• A nice blog series on the Random Oracle Model by Matthew Green

Zero Knowledge (4h)
Lecture Notes, Class Notes

• Boneh-Shoup (§20.1-20.3)
• Susan Hohenberger’s lecture notes on Fiat-Shamir

SNARKs (4h)
Lecture Notes, Class Notes

• Vitalik Buterin on “applications of ZK-SNARKs for preserving privacy”
• “Interactive Oracle Proofs” [Ben-Sasson et al., TCC'16]
• Vitalik Buterin’s “explainer on PLONK”

Private Information Retrieval (2h)
Lecture Notes, Class Notes (part 1), Class Notes (part 2)

• “A Survey of Single-Database PIR” [Ostrovsky and Skeith, 2007]
• “Private Information Retrieval with Sublinear Online Time” [Corrigan-Gibbs and Kogan, EUROCRYPT'22]
• “Apple’s deployment of PIR in production”

Oblivious RAM (2h)
Lecture Notes, Class Notes

• The original Goldreich-Ostrovsky paper
• Elaine Shi’s lecture notes

Secure multiparty computation (3h)
Lecture Notes, Class Notes

• “How to Share a Secret” [Shamir, CACM'79]
• Chapters 2 and 3 of “A Pragmatic Introduction to Secure Multi-Party Computation” [Evans et al., 2018]

Data anonymization (3h)
Lecture Notes, Class Notes

• “Linear Program Reconstruction in Practice” [Cohen & Nissim, TPDP'18]
• Section 8.1 of Dwork and Roth
• A Tweet thread by John Abowd on reconstruction attacks on the US Census

Differential Privacy (3h)
Lecture Notes, Class Notes

• “RAPPOR: Randomized Aggregatable Privacy-Preserving Ordinal Response” [Erlingsson et al., CCS'14]
• Slides on the use of “DP in US 2020 Census”
• Chapter 1 of Dwork and Roth, “The Promise of Differential Privacy”

Approximate DP (3h)
Lecture Notes, Class Notes

• Damien Desfontaines’ blog on the “Privacy Loss Random Variable” and “Why not differential privacy”
• Rogaway’s critique of DP in “The Moral Character of Cryptographic Work”

Private learning (2h)
Lecture Notes, Class Notes

• Frank McSherry on “Statistical inference considered harmful”
• Papernot and Goodfellow on “Privacy and machine learning: two unexpected allies?”

Membership Inference Attacks (4h)
Lecture Notes

• The Homer et al. paper on identifying individuals in genetic data
• Chapter 5 of Vadhan, “The Complexity of Differential Privacy”
• “Debugging Differential Privacy: A Case Study for Privacy Auditing” [Tramèr et al., 2022]

Crypto + DP (1h)

• Multi-central differential privacy [Steinke., 2020]

No class (happy holidays!)