PrivacyGo: Privacy-Preserving Ad Measurement with Multidimensional Intersection
Fuente:
arXiv
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| Auteurs principaux: | , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866908422574702592 |
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| author | Du, Jian Qian, Haohao Zhang, Shikun Lu, Wen-jie Lu, Donghang Niu, Yongchuan Jiang, Bo Zhao, Yongjun Yan, Qiang |
| author_facet | Du, Jian Qian, Haohao Zhang, Shikun Lu, Wen-jie Lu, Donghang Niu, Yongchuan Jiang, Bo Zhao, Yongjun Yan, Qiang |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_20981 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | PrivacyGo: Privacy-Preserving Ad Measurement with Multidimensional Intersection Du, Jian Qian, Haohao Zhang, Shikun Lu, Wen-jie Lu, Donghang Niu, Yongchuan Jiang, Bo Zhao, Yongjun Yan, Qiang Cryptography and Security 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. |
| title | PrivacyGo: Privacy-Preserving Ad Measurement with Multidimensional Intersection |
| topic | Cryptography and Security |
| url | https://arxiv.org/abs/2506.20981 |