Paper 2021/1490

Precio: Private Aggregate Measurement via Oblivious Shuffling

F. Betül Durak, Microsoft (United States)
Chenkai Weng, Northwestern University
Erik Anderson, Microsoft
Kim Laine, Microsoft
Melissa Chase, Microsoft
Abstract

We introduce Precio, a new secure aggregation method for computing layered histograms and sums over secret shared data in a client-server setting. Precio is motivated by ad conversion measurement scenarios, where online advertisers and ad networks want to measure the performance of ad campaigns without requiring privacy-invasive techniques, such as third-party cookies. Precio has linear (time and communication) complexity in the number of data points and guarantees differentially private outputs. We formally analyze its security and privacy and present a thorough performance evaluation. The protocol supports much larger domains than Prio. It supports much more flexible aggregates than the DPF-based solution and in some settings has up to four orders of magnitude better performance.

Metadata
Available format(s)
PDF
Category
Cryptographic protocols
Publication info
Published elsewhere. ACM CCS 2024
Keywords
secure computingsecure aggregation
Contact author(s)
betuldurak @ microsoft com
ckweng @ u northwestern edu
erikan @ microsoft com
kim laine @ microsoft com
melissac @ microsoft com
History
2024-05-22: last of 4 revisions
2021-11-15: received
See all versions
Short URL
https://ia.cr/2021/1490
License
Creative Commons Attribution
CC BY

BibTeX

@misc{cryptoeprint:2021/1490,
      author = {F.  Betül Durak and Chenkai Weng and Erik Anderson and Kim Laine and Melissa Chase},
      title = {Precio: Private Aggregate Measurement via Oblivious Shuffling},
      howpublished = {Cryptology {ePrint} Archive, Paper 2021/1490},
      year = {2021},
      url = {https://eprint.iacr.org/2021/1490}
}
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