CALM: Consistent Adaptive Local Marginal for Marginal Release under Local Differential Privacy

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Machine Learning for Synthetic Data Generation: a Review (2023, methodology): “…attribute [170] differential privacy MRF NA (w.o. DNN) attribute [171] compressive privacy GAN CNN image [172] differential privacy GAN CNN image [173] local differential privacy Maximum Entropy estimation NA (w.o. DNN) attribute [174] differential privacy Maximum Entropy estimation NA (w.o.…”
Locally Differentially Private Data Collection and Analysis (2019, background): “Current researches focus on many related problems under the LDP model, such as mean/frequency estimation [15, 39, 48], probability distribution estimation [22, 56, 47], heavy hitter identification [5, 41, 8], itemset mining [50], marginal distribution release [12, 60], and empirical risk minimizatio”
Consistent and Accurate Frequency Oracles under Local Differential Privacy (2019, background): “For another example, in [40], a few attributes are encoded together and reported using LDP, and recovering the distribution for any attribute requires computing the frequencies of sets of encoded values.”