{"id":815440,"date":"2022-01-26T11:51:21","date_gmt":"2022-01-26T19:51:21","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?post_type=msr-research-item&p=815440"},"modified":"2023-09-07T10:29:31","modified_gmt":"2023-09-07T17:29:31","slug":"aggregate-measurement-via-oblivious-shuffling","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/aggregate-measurement-via-oblivious-shuffling\/","title":{"rendered":"Precio: Private Aggregate Measurement via Oblivious Shuffling"},"content":{"rendered":"

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 (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.<\/p>\n","protected":false},"excerpt":{"rendered":"

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 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