Private Incremental Regression

Summary: Introduce private incremental ERM/regression under differential privacy, with a generic batch→incremental reduction and two streaming-regression mechanisms. One gives ~√d risk via noisy incremental gradients; the other uses random projections and Gaussian width to get ~T^{1/3}W^{2/3}, resolving adaptivity. (summarized by gpt-5-mini on Feb 09 2026)

Paper ID: 1712
Venue: PODS
Year: 2017
Pagerank: 4.8925595e-05
Overall Rank: 6,914 | 51.91%
DOI: 10.1145/3034786.3034795

Incoming Non-self Citations Over Time

Authors

Incoming Citations (Sorted by Pagerank)

Showing 1 of 1 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
11,330	Lower Bounds for Sparse Oblivious Subspace Embeddings	2022	PODS	4.1945683e-05

Outgoing Citations (Sorted by Pagerank)

Showing 3 of 3 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank	Cited Paper	Year	Venue	Pagerank
136	Revealing Information while Preserving Privacy	2003	PODS	0.0004241101
568	Practical Privacy: The SuLQ Framework	2005	PODS	0.00019949368
3,261	Private Multiplicative Weights Beyond Linear Queries	2015	PODS	7.3115472e-05

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