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Towards a Realistic Decentralized Naive Bayes with Differential Privacy
KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Software and Computer systems, SCS.ORCID iD: 0000-0002-0223-8907
Foundation for Research and Technology Hellas, Nikolaou Plastira 100, 70013, Heraklion, Greece, Nikolaou Plastira 100.
Foundation for Research and Technology Hellas, Nikolaou Plastira 100, 70013, Heraklion, Greece, Nikolaou Plastira 100.
KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Software and Computer systems, SCS.ORCID iD: 0000-0003-4516-7317
2023 (English)In: E-Business and Telecommunications - 19th International Conference, ICSBT 2022, and 19th International Conference, SECRYPT 2022, Revised Selected Papers, Springer Nature , 2023, p. 98-121Conference paper, Published paper (Refereed)
Abstract [en]

This is an extended version of our work in [16]. In this paper, we introduce two novel algorithms to collaboratively train Naive Bayes models across multiple private data sources: Federated Naive Bayes and Gossip Naive Bayes. Instead of directly providing access to their data, the data owners compute local updates that are then aggregated to build a global model. In order to also prevent indirect privacy leaks from the updates or from the final model, our algorithms protect the exchanged information with differential privacy. We experimentally evaluate our proposed approaches, examining different scenarios and focusing on potential real-world issues, such as different data owner offering different amounts of data or requesting different levels of privacy. Our results show that both Federated and Gossip Naive Bayes achieve similar accuracy to a “vanilla” Naive Bayes while maintaining reasonable privacy guarantees, while being extremely robust to heterogeneous data owners.

Place, publisher, year, edition, pages
Springer Nature , 2023. p. 98-121
Keywords [en]
Differential privacy, Federated learning, Gossip learning, Naive Bayes
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:kth:diva-339290DOI: 10.1007/978-3-031-45137-9_5Scopus ID: 2-s2.0-85174489488OAI: oai:DiVA.org:kth-339290DiVA, id: diva2:1809913
Conference
19th International Conference on Smart Business Technologies, ICSBT 2022 and International Conference on Security and Cryptography, SECRYPT 2022, Lisbon, Portugal, Jul 14 2022 - Jul 16 2022
Note

Part of ISBN 9783031451362

QC 20231106

Available from: 2023-11-06 Created: 2023-11-06 Last updated: 2023-11-06Bibliographically approved

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