國立虎尾科技大學 |

Private Computations on Streaming Data.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Private Computations on Streaming Data./
作者:	Garbe, Kevin Matthew.
面頁冊數:	1 online resource (79 pages)
附註:	Source: Dissertations Abstracts International, Volume: 85-09, Section: B.
Contained By:	Dissertations Abstracts International85-09B.
標題:	Information technology. -
電子資源:	click for full text (PQDT)
ISBN:	9798381968279

Private Computations on Streaming Data.
Garbe, Kevin Matthew.

Private Computations on Streaming Data. - 1 online resource (79 pages)

Source: Dissertations Abstracts International, Volume: 85-09, Section: B.

Thesis (Ph.D.)--University of California, Los Angeles, 2024.

Includes bibliographical references

We present a framework for privacy-preserving streaming algorithms which combine the memory-efficiency of streaming algorithms with strong privacy guarantees. These algorithms enable some number of servers to compute aggregate statistics efficiently on large quantities of user data without learning the user's inputs. While there exists limited prior work that fits within our model, our work is the first to formally define a general framework, interpret existing methods within this general framework, and develop new tools broadly applicable to this model. To highlight our model, we designed and implemented a new privacy-preserving streaming algorithm to compute heavy hitters, which are the most frequent elements in a data stream. We provide a performance comparison between our system and Poplar, the only other private statistics algorithm which supports heavy hitters. We benchmarked ours and Poplar's systems and provided direct performance comparisons within the same hardware platform. Of note, Poplar requires linear space compared to our poly-logarithmic space, meaning our system is the first to compute heavy hitters within the privacy-preserving streaming model. A small memory footprint allows our algorithm (among other benefits) to run efficiently on very large input sizes without running out of memory or crashing.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798381968279Subjects--Topical Terms:

559429
Information technology.
Subjects--Index Terms:

AlgorithmIndex Terms--Genre/Form:

554714
Electronic books.

Private Computations on Streaming Data.
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500 $a Advisor: Ostrovsky, Rafail.
502 $a Thesis (Ph.D.)--University of California, Los Angeles, 2024.
504 $a Includes bibliographical references
520 $a We present a framework for privacy-preserving streaming algorithms which combine the memory-efficiency of streaming algorithms with strong privacy guarantees. These algorithms enable some number of servers to compute aggregate statistics efficiently on large quantities of user data without learning the user's inputs. While there exists limited prior work that fits within our model, our work is the first to formally define a general framework, interpret existing methods within this general framework, and develop new tools broadly applicable to this model. To highlight our model, we designed and implemented a new privacy-preserving streaming algorithm to compute heavy hitters, which are the most frequent elements in a data stream. We provide a performance comparison between our system and Poplar, the only other private statistics algorithm which supports heavy hitters. We benchmarked ours and Poplar's systems and provided direct performance comparisons within the same hardware platform. Of note, Poplar requires linear space compared to our poly-logarithmic space, meaning our system is the first to compute heavy hitters within the privacy-preserving streaming model. A small memory footprint allows our algorithm (among other benefits) to run efficiently on very large input sizes without running out of memory or crashing.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2024
538 $a Mode of access: World Wide Web
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