his.sePublikationer
Ändra sökning
RefereraExporteraLänk till posten
Permanent länk

Direktlänk
Referera
Referensformat
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annat format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annat språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf
Addressing the challenges of privacy preserving machine learning in the context of data anonymization
Högskolan i Skövde, Institutionen för informationsteknologi. Högskolan i Skövde, Forskningscentrum för Informationsteknologi. (Skövde Artificial Intelligence Lab (SAIL))ORCID-id: 0000-0002-2564-0683
2019 (Engelska)Rapport (Övrigt vetenskapligt)
Abstract [en]

Machine learning (ML) models trained on sensitive data pose a distinct threat to privacy with the emergence of numerous threat models exploiting their privacy vulnerabilities.Therefore, privacy preserving machine learning (PPML) has gained an increased attentionover the past couple of years. Existing PPML techniques introduced in the literatureare mainly based on differential privacy or cryptography based techniques. Respectivelythey are criticized for the poor predictive accuracy of the derived ML models and for theextensive computational cost. Moreover, they operate under the assumption that originaldata are always available for training the ML models. However, there exist scenarioswhere anonymized data are available instead of the original data. Anonymization ofsensitive data is required before publishing them in order to preserve the privacy of theunderlying data subjects. Nevertheless, there are valid organizational and legal requirementsfor data publishing. In this case, it is important to understand the impact of dataanonymization on ML in general and how this can be used as a stepping stone towardsPPML.The proposed research is aimed at understanding the opportunities and challenges forPPML in the context of data anonymization, and to address them effectively by developinga unified solution to serve the objectives of both data anonymization and PPML.

Ort, förlag, år, upplaga, sidor
Skövde: University of Skövde , 2019. , s. 60
Nyckelord [en]
privacy preserving machine learning, privacy preserving data publishing, data anonymization, privacy vulnerabilities in machine learning
Nationell ämneskategori
Datorsystem
Forskningsämne
Skövde Artificial Intelligence Lab (SAIL)
Identifikatorer
URN: urn:nbn:se:his:diva-16815OAI: oai:DiVA.org:his-16815DiVA, id: diva2:1306763
Anmärkning

Research proposal, PhD programme, University of Skövde

Tillgänglig från: 2019-04-24 Skapad: 2019-04-24 Senast uppdaterad: 2019-05-02Bibliografiskt granskad

Open Access i DiVA

Fulltext saknas i DiVA

Personposter BETA

Senavirathne, Navoda

Sök vidare i DiVA

Av författaren/redaktören
Senavirathne, Navoda
Av organisationen
Institutionen för informationsteknologiForskningscentrum för Informationsteknologi
Datorsystem

Sök vidare utanför DiVA

GoogleGoogle Scholar

urn-nbn

Altmetricpoäng

urn-nbn
Totalt: 58 träffar
RefereraExporteraLänk till posten
Permanent länk

Direktlänk
Referera
Referensformat
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annat format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annat språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf