PrivacyGo: Privacy-Preserving Ad Measurement with Multidimensional Intersection

• URL: http://arxiv.org/abs/2506.20981v1
• Date: Thu, 26 Jun 2025 03:54:19 GMT
• Title: PrivacyGo: Privacy-Preserving Ad Measurement with Multidimensional Intersection
• Authors: Jian Du, Haohao Qian, Shikun Zhang, Wen-jie Lu, Donghang Lu, Yongchuan Niu, Bo Jiang, Yongjun Zhao, Qiang Yan,
• Abstract summary: This paper tackles the problem of multi-identifier private user profile matching for privacy-preserving ad measurement.<n>We introduce a comprehensive cryptographic framework leveraging reversed Oblivious Pseudorandom Functions (OPRF) and novel blind key rotation techniques.<n>Our design prevents cross-identifier linkages and includes a differentially private mechanism to obfuscate intersection sizes.
• Score: 20.83651553126354
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper tackles the challenging and practical problem of multi-identifier private user profile matching for privacy-preserving ad measurement, a cornerstone of modern advertising analytics. We introduce a comprehensive cryptographic framework leveraging reversed Oblivious Pseudorandom Functions (OPRF) and novel blind key rotation techniques to support secure matching across multiple identifiers. Our design prevents cross-identifier linkages and includes a differentially private mechanism to obfuscate intersection sizes, mitigating risks such as membership inference attacks. We present a concrete construction of our protocol that achieves both strong privacy guarantees and high efficiency. It scales to large datasets, offering a practical and scalable solution for privacy-centric applications like secure ad conversion tracking. By combining rigorous cryptographic principles with differential privacy, our work addresses a critical need in the advertising industry, setting a new standard for privacy-preserving ad measurement frameworks.

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