On the Risks of Collecting Multidimensional Data Under Local Differential Privacy

Summary: Assesses privacy threats (re-identification, attribute inference) for multidimensional data under local DP, analyzing two frequency-estimation approaches. Empirically compares five LDP protocols (GRR, local hashing, subset selection, RAPPOR, unary encoding) and proposes a countermeasure that improves utility and robustness. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID: 12981
Venue: VLDB
Year: 2023
Pagerank: 4.1945683e-05
Overall Rank: 11,227 | 21.90%
DOI: 10.14778/3579075.3579086

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Incoming Citations (Sorted by Pagerank)

Showing 1 of 1 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
10,262	Understanding Disclosure Risk in Differential Privacy with Applications to Noise Calibration and Auditing	2026	VLDB	4.1945683e-05

Outgoing Citations (Sorted by Pagerank)

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

Rank	Cited Paper	Year	Venue	Pagerank
2,540	Frequency Estimation under Local Differential Privacy	2021	VLDB	8.5797299e-05

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