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A k-Anonymised Federated Learning Framework with Decision Trees
Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.ORCID-id: 0000-0002-0368-8037
2022 (Engelska)Ingår i: Data Privacy Management, Cryptocurrencies and Blockchain Technology / [ed] Garcia-Alfaro J.; Muñoz-Tapia J.L.; Navarro-Arribas G.; Soriano M., Springer Science+Business Media B.V., 2022, Vol. 13140, s. 106-120Konferensbidrag, Publicerat paper (Refereegranskat)
Abstract [en]

We propose a privacy-preserving framework using Mondrian k-anonymity with decision trees in a Federated Learning (FL) setting for the horizontally partitioned data. Data heterogeneity in FL makes the data non-IID (Non-Independent and Identically Distributed). We use a novel approach to create non-IID partitions of data by solving an optimization problem. In this work, each device trains a decision tree classifier. Devices share the root node of their trees with the aggregator. The aggregator merges the trees by choosing the most common split attribute and grows the branches based on the split values of the chosen split attribute. This recursive process stops when all the nodes to be merged are leaf nodes. After the merging operation, the aggregator sends the merged decision tree to the distributed devices. Therefore, we aim to build a joint machine learning model based on the data from multiple devices while offering k-anonymity to the participants.

Ort, förlag, år, upplaga, sidor
Springer Science+Business Media B.V., 2022. Vol. 13140, s. 106-120
Serie
Lecture Notes in Computer Science, ISSN 03029743, E-ISSN 16113349
Nyckelord [en]
Aggregation, Decision tree, Federated learning, Privacy
Nationell ämneskategori
Datavetenskap (datalogi) Datorsystem
Identifikatorer
URN: urn:nbn:se:umu:diva-192745DOI: 10.1007/978-3-030-93944-1_7ISI: 000754558600007Scopus ID: 2-s2.0-85124654159ISBN: 9783030939434 (tryckt)OAI: oai:DiVA.org:umu-192745DiVA, id: diva2:1640455
Konferens
16th International Workshop on Data Privacy Management, DPM 2021, and 5th International Workshop on Cryptocurrencies and Blockchain Technology, CBT 2021 held in conjunction with ESORICS 2021, Online, 8 October, 2021.
Tillgänglig från: 2022-02-24 Skapad: 2022-02-24 Senast uppdaterad: 2023-09-05Bibliografiskt granskad

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Kwatra, SaloniTorra, Vicenç

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