umu.sePublikationer
Ändra sökning
RefereraExporteraLänk till posten
Permanent länk

Direktlänk
Referera
Referensformat
  • apa
  • 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
dpUGC: Learn Differentially Private Representation for User Generated Contents
Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap. (Database and Data Mining Group)ORCID-id: 0000-0001-8820-2405
(University of Tasmania, Australia)
Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap. (Database and Data Mining Group)
2019 (Engelska)Ingår i: Proceedings of the 20th International Conference on Computational Linguistics and Intelligent Text Processing (CICLing), 2019, 2019Konferensbidrag, Publicerat paper (Refereegranskat)
Abstract [en]

This paper firstly proposes a simple yet efficient generalized approach to apply differential privacy to text representation (i.e., word embedding). Based on it, we propose a user-level approach to learn personalized differentially private word embedding model on user generated contents (UGC). To our best knowledge, this is the first work of learning user-level differentially private word embedding model from text for sharing. The proposed approaches protect the privacy of the individual from re-identification, especially provide better trade-off of privacy and data utility on UGC data for sharing. The experimental results show that the trained embedding models are applicable for the classic text analysis tasks (e.g., regression). Moreover, the proposed approaches of learning differentially private embedding models are both framework- and dataindependent, which facilitates the deployment and sharing. The source code is available at https://github.com/sonvx/dpText.

Ort, förlag, år, upplaga, sidor
2019.
Nationell ämneskategori
Språkteknologi (språkvetenskaplig databehandling)
Identifikatorer
URN: urn:nbn:se:umu:diva-160887OAI: oai:DiVA.org:umu-160887DiVA, id: diva2:1330435
Konferens
20th International Conference on Computational Linguistics and Intelligent Text Processing, La Rochelle, France, April 7-13, 2019
Tillgänglig från: 2019-06-25 Skapad: 2019-06-25 Senast uppdaterad: 2019-08-22
Ingår i avhandling
1. Privacy-awareness in the era of Big Data and machine learning
Öppna denna publikation i ny flik eller fönster >>Privacy-awareness in the era of Big Data and machine learning
2019 (Engelska)Licentiatavhandling, sammanläggning (Övrigt vetenskapligt)
Alternativ titel[sv]
Integritetsmedvetenhet i eran av Big Data och maskininlärning
Abstract [en]

Social Network Sites (SNS) such as Facebook and Twitter, have been playing a great role in our lives. On the one hand, they help connect people who would not otherwise be connected before. Many recent breakthroughs in AI such as facial recognition [49] were achieved thanks to the amount of available data on the Internet via SNS (hereafter Big Data). On the other hand, due to privacy concerns, many people have tried to avoid SNS to protect their privacy. Similar to the security issue of the Internet protocol, Machine Learning (ML), as the core of AI, was not designed with privacy in mind. For instance, Support Vector Machines (SVMs) try to solve a quadratic optimization problem by deciding which instances of training dataset are support vectors. This means that the data of people involved in the training process will also be published within the SVM models. Thus, privacy guarantees must be applied to the worst-case outliers, and meanwhile data utilities have to be guaranteed.

For the above reasons, this thesis studies on: (1) how to construct data federation infrastructure with privacy guarantee in the big data era; (2) how to protect privacy while learning ML models with a good trade-off between data utilities and privacy. To the first point, we proposed different frameworks em- powered by privacy-aware algorithms that satisfied the definition of differential privacy, which is the state-of-the-art privacy-guarantee algorithm by definition. Regarding (2), we proposed different neural network architectures to capture the sensitivities of user data, from which, the algorithm itself decides how much it should learn from user data to protect their privacy while achieves good performance for a downstream task. The current outcomes of the thesis are: (1) privacy-guarantee data federation infrastructure for data analysis on sensitive data; (2) privacy-guarantee algorithms for data sharing; (3) privacy-concern data analysis on social network data. The research methods used in this thesis include experiments on real-life social network dataset to evaluate aspects of proposed approaches.

Insights and outcomes from this thesis can be used by both academic and industry to guarantee privacy for data analysis and data sharing in personal data. They also have the potential to facilitate relevant research in privacy-aware representation learning and related evaluation methods.

Ort, förlag, år, upplaga, sidor
Umeå: Department of computing science, Umeå University, 2019. s. 42
Serie
Report / UMINF, ISSN 0348-0542 ; 19.06
Nyckelord
Diferential Privacy, Machine Learning, Deep Learning, Big Data
Nationell ämneskategori
Datavetenskap (datalogi)
Forskningsämne
datalogi
Identifikatorer
urn:nbn:se:umu:diva-162182 (URN)9789178551101 (ISBN)
Presentation
2019-09-09, 23:40 (Engelska)
Handledare
Tillgänglig från: 2019-08-22 Skapad: 2019-08-15 Senast uppdaterad: 2019-08-26Bibliografiskt granskad

Open Access i DiVA

Fulltext saknas i DiVA

Personposter BETA

Vu, Xuan-SonLili, Jiang

Sök vidare i DiVA

Av författaren/redaktören
Vu, Xuan-SonLili, Jiang
Av organisationen
Institutionen för datavetenskap
Språkteknologi (språkvetenskaplig databehandling)

Sök vidare utanför DiVA

GoogleGoogle Scholar

urn-nbn

Altmetricpoäng

urn-nbn
Totalt: 26 träffar
RefereraExporteraLänk till posten
Permanent länk

Direktlänk
Referera
Referensformat
  • apa
  • 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