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Integrally private model selection for support vector machine
Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.ORCID-id: 0000-0002-8073-6784
Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.ORCID-id: 0000-0002-0368-8037
2024 (Engelska)Ingår i: Computer Security. ESORICS 2023 International Workshops: CyberICS, DPM, CBT, and SECPRE, The Hague, The Netherlands, September 25–29, 2023, Revised Selected Papers, Part I / [ed] Sokratis Katsikas; Frédéric Cuppens; Nora Cuppens-Boulahia; Costas Lambrinoudakis; Joaquin Garcia-Alfaro; Guillermo Navarro-Arribas; Pantaleone Nespoli; Christos Kalloniatis; John Mylopoulos; Annie Antón; Stefanos Gritzalis, Springer Nature, 2024, s. 249-259Konferensbidrag, Publicerat paper (Refereegranskat)
Abstract [en]

Today, there are unlimited applications of data mining techniques. According to ongoing privacy regulations, data mining techniques that preserve users’ privacy are a primary requirement. Our work contributes to the Privacy-Preserving Data Mining (PPDM) domain. We work with Integral Privacy, which provides users with private machine learning model recommendations and privacy against model comparison attacks. For machine learning, we work with Support Vector Machine (SVM), which is based on the structural risk minimization principle. Our experiments show that we obtain highly recurrent SVM models due to their peculiar properties, requiring only a subset of the training data to learn well. Not only high recurrence, but from our empirical results, we show that integrally private SVM models obtain good results in accuracy, recall, precision, and F1-score compared with the baseline SVM model and the ϵ Differentially Private SVM (DPSVM) model.

Ort, förlag, år, upplaga, sidor
Springer Nature, 2024. s. 249-259
Serie
Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349
Nyckelord [en]
Differential Privacy, Integral Privacy, Privacy-Preserving Data Mining, Support Vector Machine
Nationell ämneskategori
Datavetenskap (datalogi)
Identifikatorer
URN: urn:nbn:se:umu:diva-222652DOI: 10.1007/978-3-031-54204-6_14Scopus ID: 2-s2.0-85187781947ISBN: 978-3-031-54203-9 (tryckt)ISBN: 978-3-031-54204-6 (digital)OAI: oai:DiVA.org:umu-222652DiVA, id: diva2:1852806
Konferens
International Workshops which were held in conjunction with 28th European Symposium on Research in Computer Security, ESORICS 2023, The HAgue, The Netherlands, September 25-29, 2023
Tillgänglig från: 2024-04-19 Skapad: 2024-04-19 Senast uppdaterad: 2024-04-19Bibliografiskt granskad

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Kwatra, SaloniVarshney, Ayush K.Torra, Vicenç

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Totalt: 41 träffar
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