CS 6434: Digital Privacy

This course is a survey of state-of-the-art research on digital privacy, including computing technologies that both undermine and protect digital privacy. A significant component of the course involves analyzing privacy issues from the perspective of contextual integrity and other philosophical, legal, and social approaches, providing students with an understanding of why certain activities, technologies, and systems are considered to be privacy threats and/or violations, while others are seen as helpful in protecting and promoting privacy. Upon completion, students will be able to:

• Describe privacy as a philosophical concept and an ethical value;
• Understand advanced computer science research on threats to digital privacy and privacy protection; and
• Analyze benefits and limitations of privacy protection technologies.

 

Aug 22. Course overview. What is "privacy"?

• Optional: Warren and Brandeis. "The Right to Privacy".

Aug 24. Privacy as control over information.

• Read: "Privacy in Context", chapters 4 and 5.

Aug 29. Decentralized information flow control.

• Read: Myers and Liskov. "Protecting Privacy using the Decentralized Label Model".

Aug 31. Privacy-respecting Web services.

• Read: Wang, Ko, and Mickens. "Riverbed: Enforcing User-defined Privacy Constraints in Distributed Web Services".

Sep 5. Web tracking and advertising.

• Read: Mayer and Mitchell. "Third-Party Web Tracking: Policy and Technology".
• Read: Korolova. "Privacy Violations Using Microtargeted Ads: A Case Study".
• Optional: Bashir and Wilson. "Diffusion of User Tracking Data in the Online Advertising Ecosystem".

Sep 7. Google's Privacy Sandbox.

• Read: Digging into the Privacy Sandbox.

Sep 12. Mobile app privacy.

• Read: Reardon et al. "50 Ways to Leak Your Data: An Exploration of Apps’ Circumvention of the Android Permissions System".

Sep 14. CS colloquium: Privacy-preserving machine learning with CrypTen and Fisher information loss.

Sep 19. Location privacy.

• Read: de Montjoye et al. "Unique in the Crowd: The Privacy Bounds of Human Mobility".

Sep 21. Data anonymization and re-identification.

• Read: Sweeney. "Weaving Technology and Policy Together to Maintain Confidentiality".
• Read: Narayanan and Shmatikov. "Robust De-anonymization of Large Sparse Datasets".
• Optional: Fellegi and Sunter. "A Theory of Record Linkage".
• Optional: Dalenius. "Finding a Needle In a Haystack or Identifying Anonymous Census Records".

Sep 26. Impossibility of database privacy.

• Read: Dinur and Nissim. "Revealing Information while Preserving Privacy".

Sep 28. Differential privacy.

• Read: Dwork et al. "Calibrating Noise to Sensitivity in Private Data Analysis".
• Read: McSherry. "Privacy Integrated Queries".
• Read: Seeman and Susser. "Between Privacy and Utility: On Differential Privacy in Theory and Practice".

Oct 3. Privacy as contextual integrity.

• Read: "Privacy in Context", chapter 7.

Oct 5. DLI seminar: No cookies for you: Evaluating the promises of Big Tech’s “privacy-enhancing” techniques.

Oct 12. Differential privacy (cont'd).

Oct 17. Local differential privacy.

• Read: Erlingsson et al. "RAPPOR: Randomized Aggregatable Privacy-Preserving Ordinal Response".

Oct 19. DLI seminar: OpenDP: A community effort to advance the practice of differential privacy.

Oct 24. Privacy-preserving aggregation (using differential privacy).

• Read: Roth et al. "Honeycrisp: Large-Scale Differentially Private Aggregation Without a Trusted Core".
• Optional: Roth et al. "Orchard: Differentially Private Analytics at Scale".

Oct 26. Privacy-preserving aggregation (using secret-sharing).

• Read: Corrigan-Gibbs and Boneh. "Prio: Private, Robust, and Scalable Computation of Aggregate Statistics".
• Read: Case et al. "Interoperable Private Attribution: A Distributed Attribution and Aggregation Protocol".

Oct 31. Genomic privacy.

• Read: Gymrek et al. "Identifying Personal Genomes by Surname Inference".
• Optional: Erlich and Narayanan. "Routes For Breaching and Protecting Genetic Privacy".

Nov 2. Privacy violations in machine learning.

• Read: Shokri et al. "Membership Inference Attacks Against Machine Learning Models".
• Read: Carlini et al. "Extracting Training Data from Large Language Models".
• Optional: Carlini et al. "Membership Inference Attacks From First Principles".

Nov 7. Differentially private machine learning.

• Read: Abadi et al. "Deep Learning with Differential Privacy".
• Read: Jayaraman and Evans. "Evaluating Differentially Private Machine Learning in Practice".
• Optional: Tramèr and Boneh. "Differentially Private Learning Needs Better Features (or Much More Data)".

Nov 9. Federated learning.

• Read: McMahan et al. "Communication-Efficient Learning of Deep Networks from Decentralized Data".

Nov 14. End-to-end encrypted communications.

• Read: Abelson et al. "Keys Under Doormats: Mandating Insecurity by Requiring Government Access to All Data and Communications".
• Optional: Cohn-Gordon et al. "A Formal Security Analysis of the Signal Messaging Protocol"

Nov 16. Anonymity networks.

• Read: Dingledine et al. "Tor: The Second-Generation Onion Router".

Nov 21. Censorship resistance.

• Read: Barradas et al. "Poking a Hole in the Wall: Efficient Censorship-Resistant Internet Communications by Parasitizing on WebRTC".

Nov 28. The Fourth Amendment. Legal concepts of privacy.

• Read: Solove and Schwartz. "Privacy Law Fundamentals".
• Read: Hecht-Felella. "The Fourth Amendment in the Digital Age".

Nov 30. Face recognition. Privacy of biometrics.