Differential Privacy on Fully Dynamic Streams

Yuan QIU; Ke YI

Differential Privacy on Fully Dynamic Streams

Yuan QIU
, Ke YI

Department of Computer Science and Engineering

Research output: Contribution to conference › Conference Paper › peer-review

Abstract

A fundamental problem in differential privacy is to release privatized answers to a class of linear queries with small error. This problem has been well studied in the static case. In this paper, we consider the fully dynamic setting where items may be inserted into or deleted from the dataset over time, and we need to continually release query answers at every time instance. We present efficient black-box constructions of such dynamic differentially private mechanisms from static ones with only a polylogarithmic degradation in the utility.

Original language	English
Publication status	Published - 2025
Event	39th Annual Conference on Neural Information Processing Systems, NeurIPS 2025 - San Diego, United States Duration: 2 Dec 2025 → 7 Dec 2025

Conference

Conference	39th Annual Conference on Neural Information Processing Systems, NeurIPS 2025
Country/Territory	United States
City	San Diego
Period	2/12/25 → 7/12/25

Cite this

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title = "Differential Privacy on Fully Dynamic Streams",

abstract = "A fundamental problem in differential privacy is to release privatized answers to a class of linear queries with small error. This problem has been well studied in the static case. In this paper, we consider the fully dynamic setting where items may be inserted into or deleted from the dataset over time, and we need to continually release query answers at every time instance. We present efficient black-box constructions of such dynamic differentially private mechanisms from static ones with only a polylogarithmic degradation in the utility. ",

author = "Yuan QIU and Ke YI",

year = "2025",

language = "English",

note = "39th Annual Conference on Neural Information Processing Systems, NeurIPS 2025 ; Conference date: 02-12-2025 Through 07-12-2025",

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TY - CONF

T1 - Differential Privacy on Fully Dynamic Streams

AU - QIU, Yuan

AU - YI, Ke

PY - 2025

Y1 - 2025

N2 - A fundamental problem in differential privacy is to release privatized answers to a class of linear queries with small error. This problem has been well studied in the static case. In this paper, we consider the fully dynamic setting where items may be inserted into or deleted from the dataset over time, and we need to continually release query answers at every time instance. We present efficient black-box constructions of such dynamic differentially private mechanisms from static ones with only a polylogarithmic degradation in the utility.

AB - A fundamental problem in differential privacy is to release privatized answers to a class of linear queries with small error. This problem has been well studied in the static case. In this paper, we consider the fully dynamic setting where items may be inserted into or deleted from the dataset over time, and we need to continually release query answers at every time instance. We present efficient black-box constructions of such dynamic differentially private mechanisms from static ones with only a polylogarithmic degradation in the utility.

M3 - Conference Paper

T2 - 39th Annual Conference on Neural Information Processing Systems, NeurIPS 2025

Y2 - 2 December 2025 through 7 December 2025

ER -

Differential Privacy on Fully Dynamic Streams

Abstract

Conference

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