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Separating Oblivious and Adaptive Differential Privacy under Continual Observation

Authors: Mark Bun, Marco Gaboardi, Connor Wagaman

We resolve an open question of Jain, Raskhodnikova, Sivakumar, and Smith (ICML 2023) by exhibiting a problem separating differential privacy under continual observation in the oblivious and adaptive settings. The continual observation (a.k.a. continual release) model formalizes privacy for streaming algorithms, where data is received over…

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Local Node Differential Privacy

Authors: Sofya Raskhodnikova, Adam Smith, Connor Wagaman, Anatoly Zavyalov

We initiate an investigation of node differential privacy for graphs in the local model of private data analysis. In our model, dubbed LNDP, each node sees its own edge list and releases the output of a local randomizer on this input. These outputs are aggregated by an untrusted server to obtain a final output. We…

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