Open Research Challenges for Private Advertising Systems Under Local Differential Privacy

Matilde Tullii; Solenne Gaucher; Hugo Richard; Eustache Diemert; Vianney Perchet; Alain Rakotomamonjy; Clément Calauzènes; Maxime Vono

doi:10.1007/978-981-96-0576-7_9

Open Research Challenges for Private Advertising Systems Under Local Differential Privacy

Matilde Tullii, Solenne Gaucher, Hugo Richard, Eustache Diemert, Vianney Perchet, Alain Rakotomamonjy, Clément Calauzènes, Maxime Vono

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Due to the ongoing deprecation of third-party cookies on mainstream browsers, the digital advertising industry is facing novel challenges regarding how to operate artificial intelligence (AI) systems. One of these bottlenecks lies in the tentative use of local differential privacy (LDP) to obfuscate granular user data, preventing from using standard machine learning pipelines to tackle the privacy/utility trade-off. This position paper reviews the main research directions that have been explored to cope with this issue and states the main positioning and research guidelines regarding how to operate an AI system under LDP, notably by pointing out the main limitations of existing work. More specifically, we highlight the importance of conducting research works focusing on multi-task learning under LDP schemes and of seeking prior information to help design privacy-preserving mechanisms.

Original language	English
Title of host publication	Web Information Systems Engineering – WISE 2024 - 25th International Conference, Proceedings
Editors	Mahmoud Barhamgi, Hua Wang, Xin Wang
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	107-122
Number of pages	16
ISBN (Print)	9789819605750
DOIs	https://doi.org/10.1007/978-981-96-0576-7_9
Publication status	Published - 1 Jan 2025
Externally published	Yes
Event	25th International Conference on Web Information Systems Engineering, WISE 2024 - Doha, Qatar Duration: 2 Dec 2024 → 5 Dec 2024

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	15440 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	25th International Conference on Web Information Systems Engineering, WISE 2024
Country/Territory	Qatar
City	Doha
Period	2/12/24 → 5/12/24

Keywords

Advertising systems
Continuous learning
Differential Privacy

Access to Document

10.1007/978-981-96-0576-7_9

Cite this

Tullii, M., Gaucher, S., Richard, H., Diemert, E., Perchet, V., Rakotomamonjy, A., Calauzènes, C., & Vono, M. (2025). Open Research Challenges for Private Advertising Systems Under Local Differential Privacy. In M. Barhamgi, H. Wang, & X. Wang (Eds.), Web Information Systems Engineering – WISE 2024 - 25th International Conference, Proceedings (pp. 107-122). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 15440 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-981-96-0576-7_9

Tullii, Matilde ; Gaucher, Solenne ; Richard, Hugo et al. / Open Research Challenges for Private Advertising Systems Under Local Differential Privacy. Web Information Systems Engineering – WISE 2024 - 25th International Conference, Proceedings. editor / Mahmoud Barhamgi ; Hua Wang ; Xin Wang. Springer Science and Business Media Deutschland GmbH, 2025. pp. 107-122 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "Due to the ongoing deprecation of third-party cookies on mainstream browsers, the digital advertising industry is facing novel challenges regarding how to operate artificial intelligence (AI) systems. One of these bottlenecks lies in the tentative use of local differential privacy (LDP) to obfuscate granular user data, preventing from using standard machine learning pipelines to tackle the privacy/utility trade-off. This position paper reviews the main research directions that have been explored to cope with this issue and states the main positioning and research guidelines regarding how to operate an AI system under LDP, notably by pointing out the main limitations of existing work. More specifically, we highlight the importance of conducting research works focusing on multi-task learning under LDP schemes and of seeking prior information to help design privacy-preserving mechanisms.",

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Tullii, M, Gaucher, S, Richard, H, Diemert, E, Perchet, V, Rakotomamonjy, A, Calauzènes, C & Vono, M 2025, Open Research Challenges for Private Advertising Systems Under Local Differential Privacy. in M Barhamgi, H Wang & X Wang (eds), Web Information Systems Engineering – WISE 2024 - 25th International Conference, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 15440 LNCS, Springer Science and Business Media Deutschland GmbH, pp. 107-122, 25th International Conference on Web Information Systems Engineering, WISE 2024, Doha, Qatar, 2/12/24. https://doi.org/10.1007/978-981-96-0576-7_9

Open Research Challenges for Private Advertising Systems Under Local Differential Privacy. / Tullii, Matilde; Gaucher, Solenne; Richard, Hugo et al.
Web Information Systems Engineering – WISE 2024 - 25th International Conference, Proceedings. ed. / Mahmoud Barhamgi; Hua Wang; Xin Wang. Springer Science and Business Media Deutschland GmbH, 2025. p. 107-122 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 15440 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Richard, Hugo

AU - Diemert, Eustache

AU - Perchet, Vianney

AU - Rakotomamonjy, Alain

AU - Calauzènes, Clément

AU - Vono, Maxime

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N2 - Due to the ongoing deprecation of third-party cookies on mainstream browsers, the digital advertising industry is facing novel challenges regarding how to operate artificial intelligence (AI) systems. One of these bottlenecks lies in the tentative use of local differential privacy (LDP) to obfuscate granular user data, preventing from using standard machine learning pipelines to tackle the privacy/utility trade-off. This position paper reviews the main research directions that have been explored to cope with this issue and states the main positioning and research guidelines regarding how to operate an AI system under LDP, notably by pointing out the main limitations of existing work. More specifically, we highlight the importance of conducting research works focusing on multi-task learning under LDP schemes and of seeking prior information to help design privacy-preserving mechanisms.

AB - Due to the ongoing deprecation of third-party cookies on mainstream browsers, the digital advertising industry is facing novel challenges regarding how to operate artificial intelligence (AI) systems. One of these bottlenecks lies in the tentative use of local differential privacy (LDP) to obfuscate granular user data, preventing from using standard machine learning pipelines to tackle the privacy/utility trade-off. This position paper reviews the main research directions that have been explored to cope with this issue and states the main positioning and research guidelines regarding how to operate an AI system under LDP, notably by pointing out the main limitations of existing work. More specifically, we highlight the importance of conducting research works focusing on multi-task learning under LDP schemes and of seeking prior information to help design privacy-preserving mechanisms.

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Tullii M, Gaucher S, Richard H, Diemert E, Perchet V, Rakotomamonjy A et al. Open Research Challenges for Private Advertising Systems Under Local Differential Privacy. In Barhamgi M, Wang H, Wang X, editors, Web Information Systems Engineering – WISE 2024 - 25th International Conference, Proceedings. Springer Science and Business Media Deutschland GmbH. 2025. p. 107-122. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-981-96-0576-7_9