Paper 2021/1490

Aggregate Measurement via Oblivious Shuffling

Erik Anderson, Microsoft
Melissa Chase, Microsoft
Wei Dai, Microsoft
F. Betul Durak, Microsoft
Kim Laine, Microsoft
Siddhart Sharma, Microsoft
Chenkai Weng, Northwestern University

We introduce a new secure aggregation method for computing aggregate statistics over secret shared data in a client-server setting. Our protocol is particularly suitable for ad conversion measurement computations, where online advertisers and ad networks want to measure the performance of ad campaigns without requiring privacy-invasive techniques, such as third-party cookies. Our protocol has linear complexity in the number of data points and guarantees differentially private outputs. We formally analyze the security and privacy of our protocol and present a performance evaluation with comparison to other approaches proposed for a similar task.

Available format(s)
Cryptographic protocols
Publication info
secure computing secure aggregation
Contact author(s)
erikan @ microsoft com
mellisac @ microsoft com
wei dai @ microsoft com
betuldurak @ microsoft com
kim laine @ microsoft com
siddhash @ microsoft com
ckweng @ u northwestern edu
2022-08-08: revised
2021-11-15: received
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Creative Commons Attribution


      author = {Erik Anderson and Melissa Chase and Wei Dai and F.  Betul Durak and Kim Laine and Siddhart Sharma and Chenkai Weng},
      title = {Aggregate Measurement via Oblivious Shuffling},
      howpublished = {Cryptology ePrint Archive, Paper 2021/1490},
      year = {2021},
      note = {\url{}},
      url = {}
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