Schedule | CS561 Topics in Data Privacy

Tentative Schedule

Week/Dates	Tuesday	Thursday	Reading Materials
W1, 08/20 - 08/23	Introduction	Reidentification Attacks	k-Anonymity: a Model for Protecting Privacy l-Diversity: Privacy Beyond k-Anonymity t-Closeness: Privacy Beyond k-Anonymity and l-Diversity Composition Attacks and Auxiliary Information in Data Privacy Minimality Attack in Privacy Preserving Data Publishing
W2, 08/26 - 08/30	Reconstruction Attacks	Reconstruction Attacks (cont.)	Robust De-anonymization of Large Sparse Datasets Wherefore Art Thou R3579X? Anonymized Social Networks, Hidden Patterns, and Structural Steganography De-anonymizing Social Networks Link Prediction by De-anonymization: How We Won the Kaggle Social Network Challenge Understanding Database Reconstruction Attacks on Public Data Revealing Information while Preserving Privacy Linear Program Reconstruction in Practice
W3, 09/02 - 09/06	Secure Multi-Party Computation (MPC)	MPC (cont.)	New Directions in Cryptography A Public Key Cryptosystem and a Signature Scheme Based on Discrete Logarithms A Randomized Protocol for Signing Contracts Protocols for Secure Computations
W4, 09/09 - 09/13	Homomorphic Encryption (HE)	Zero Knowledge Proofs (ZK)	Fully Homomorphic Encryption Using Ideal Lattices Fully Homomorphic Encryption over the Integers Implementing Gentry’s Fully-Homomorphic Encryption Scheme Efficient Fully Homomorphic Encryption from (Standard) LWE (Leveled) Fully Homomorphic Encryption without Bootstrapping Fully Homomorphic SIMD Operations Homomorphic Encryption from Learning with Errors: Conceptually-Simpler, Asymptotically-Faster, Attribute-Based Lattice-Based FHE as Secure as PKE FHEW: Bootstrapping Homomorphic Encryption in Less than a Second Homomorphic Encryption for Arithmetic of Approximate Numbers TFHE: Fast Fully Homomorphic Encryption over the Torus Secure Large-Scale Genome-Wide Association Studies Using Homomorphic Encryption CryptoNets: Applying Neural Networks to Encrypted Data with High Throughput and Accuracy The Knowledge Complexity of Interactive Proof Systems A Note on Efficient Zero-knowledge Proofs and Arguments Computationally Sound Proofs Pinocchio: Nearly Practical Verifiable Computation On the Size of Pairing-based Non-interactive Arguments Scalable, Transparent, and Post-Quantum Secure Computational Integrity
W5, 09/16 - 09/20	Intro to Differential Privacy (DP)	Intro to DP	Calibrating Noise to Sensitivity in Private Data Analysis Our Data, Ourselves: Privacy via Distributed Noise Generation Mechanism Design via Differential Privacy
W6, 09/23 - 09/27	DP Mechanisms	DP Mechanisms	On Significance of the Least Significant Bits For Differential Privacy Renyi Differential Privacy Randomized Response: A Survey Technique for Eliminating Evasive Answer Bias Universally Utility-Maximizing Privacy Mechanisms Understanding the Sparse Vector Technique for Differential Privacy Boosting the Accuracy of Differentially Private Histograms through Consistency Differential Privacy via Wavelet Transforms Privtree: A Differentially Private Algorithm for Hierarchical Decompositions Differentially Private Spatial Decompositions A Data-and-Workload-Aware Algorithm for Range Queries under Differential Privacy Differentially Private k-Means Clustering RAPPOR: Randomized Aggregatable Privacy-Preserving Ordinal Response Locally Differentially Private Protocols for Frequency Estimation
W7, 09/30 - 10/04	No Class (Fall break)	No Class (Fall break)
W8, 10/07 - 10/11	No Class (Friday classes meet)	More DP Mechanisms
W9, 10/14 - 10/18	More DP Mechanisms	Privacy Preserving Machine Learning
W10, 10/21 - 10/25	Presentation	Presentation
W11, 10/28 - 11/01	Presentation	Presentation
W12, 11/04 - 11/08	Presentation	Presentation
W13, 11/11 - 11/15	Presentation	Presentation
W14, 11/18 - 11/22	Presentation	Presentation
W15, 11/25 - 11/29	No Class (Friday classes meet)	No Class (Thanksgiving)
W16, 12/02 - 12/05	Final Project Presentation	Final Project Presentation