Precio: Private Aggregate Measurement via Oblivious Shuffling
Erik Anderson, Melissa Chase, F. Betül Durak, Kim Laine, Chenkai Weng
2024年份
3被引次数
5顶会引用
摘要
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.
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- Hash-Prune-Invert: Improved Differentially Private Heavy-Hitter Detection in the Two-Server ModelBorja Balle, James Bell-Clark, Albert Cheu, Adrià Gascón 等S&P 2025
- Distributed Synthesis of Differentially Private Tabular DatasetsYucheng Fu, Tianyao Gu, Elaine Shi, Tianhao WangUSENIX Security 2026
- OBLIVIATOR: OBLIVIous Parallel Joins and other OperATORs in Shared Memory EnvironmentsApostolos Mavrogiannakis, Xian Wang, Ioannis Demertzis, Dimitrios Papadopoulos 等USENIX Security 2025
- FLOSS: Fast Linear Online Secret-Shared ShufflingIan Chang, Sela Navot, Alex Ozdemir, Nirvan TyagiUSENIX Security 2026
- Doppio: Communication-Efficient and Secure Multi-Party Shuffle Differential PrivacyWentao Dong, Yang Cao, Cong Wang, Wei-Bin LeeVLDB 2026
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