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Explore a 23-minute conference talk on constructing marginal tables from multi-dimensional user data while adhering to Local Differential Privacy (LDP). Delve into the CALM (Consistent Adaptive Local Marginal) protocol, which addresses privacy concerns without relying on trusted third parties. Learn about data collection methods, LDP deployment and applications, and various techniques including Random Response, Strawman Method 2 (AM), and Fourier Transformation Method (FT). Discover how CALM ensures consistency between noisy marginals, constructs k-way marginals, and selects appropriate marginal sets. Examine experimental results on binary and non-binary datasets, assessing performance through Sum of Squared Errors (SSE) and classification accuracy.
