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

ENSAE
ENSAE & Criteo AI Lab.

Résultats de recherche: Le chapitre dans un livre, un rapport, une anthologie ou une collection › Contribution à une conférence › Revue par des pairs

Résumé

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.

langue originale	Anglais
titre	Web Information Systems Engineering – WISE 2024 - 25th International Conference, Proceedings
rédacteurs en chef	Mahmoud Barhamgi, Hua Wang, Xin Wang
Editeur	Springer Science and Business Media Deutschland GmbH
Pages	107-122
Nombre de pages	16
ISBN (imprimé)	9789819605750
Les DOIs	https://doi.org/10.1007/978-981-96-0576-7_9
état	Publié - 1 janv. 2025
Modification externe	Oui
Evénement	25th International Conference on Web Information Systems Engineering, WISE 2024 - Doha, Qatar Durée: 2 déc. 2024 → 5 déc. 2024

Série de publications

Nom	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	15440 LNCS
ISSN (imprimé)	0302-9743
ISSN (Electronique)	1611-3349

Une conférence

Une conférence	25th International Conference on Web Information Systems Engineering, WISE 2024
Pays/Territoire	Qatar
La ville	Doha
période	2/12/24 → 5/12/24

Accès au document

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

Contient cette citation

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. Dans M. Barhamgi, H. Wang, & X. Wang (eds.), Web Information Systems Engineering – WISE 2024 - 25th International Conference, Proceedings (p. 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. Editeur / 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) ).

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title = "Open Research Challenges for Private Advertising Systems Under Local Differential Privacy",

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. Dans 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, p. 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).

Résultats de recherche: Le chapitre dans un livre, un rapport, une anthologie ou une collection › Contribution à une conférence › Revue par des pairs

TY - GEN

T1 - Open Research Challenges for Private Advertising Systems Under Local Differential Privacy

AU - Tullii, Matilde

AU - Gaucher, Solenne

AU - Richard, Hugo

AU - Diemert, Eustache

AU - Perchet, Vianney

AU - Rakotomamonjy, Alain

AU - Calauzènes, Clément

AU - Vono, Maxime

N1 - Publisher Copyright: © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

PY - 2025/1/1

Y1 - 2025/1/1

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.

KW - Advertising systems

KW - Continuous learning

KW - Differential Privacy

U2 - 10.1007/978-981-96-0576-7_9

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

M3 - Conference contribution

AN - SCOPUS:85211245196

SN - 9789819605750

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 107

EP - 122

BT - Web Information Systems Engineering – WISE 2024 - 25th International Conference, Proceedings

A2 - Barhamgi, Mahmoud

A2 - Wang, Hua

A2 - Wang, Xin

PB - Springer Science and Business Media Deutschland GmbH

T2 - 25th International Conference on Web Information Systems Engineering, WISE 2024

Y2 - 2 December 2024 through 5 December 2024

ER -

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. Dans Barhamgi M, Wang H, Wang X, rédacteurs en chef, 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

Open Research Challenges for Private Advertising Systems Under Local Differential Privacy

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