Sökning: onr:"swepub:oai:DiVA.org:umu-160887" >
dpUGC :
dpUGC : learn differentially private representation for user generated contents
-
- Vu, Xuan-Son, 1988- (författare)
- Umeå universitet,Institutionen för datavetenskap,Database and Data Mining Group
-
- Tran, Son N. (författare)
- ICT Discipline, University of Tasmania, Hobart, Australia
-
- Jiang, Lili (författare)
- Umeå universitet,Institutionen för datavetenskap,Database and Data Mining Group
-
(creator_code:org_t)
- 2023-02-26
- 2023
- Engelska.
-
Ingår i: Computational linguistics and intelligent text processing. - Cham : Springer. - 9783031243363 - 9783031243370 ; , s. 316-331
- Relaterad länk:
-
http://arxiv.org/abs...
-
visa fler...
-
https://urn.kb.se/re...
-
https://doi.org/10.1...
-
visa färre...
Abstract
Ämnesord
Stäng
- 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.
Ämnesord
- NATURVETENSKAP -- Data- och informationsvetenskap -- Språkteknologi (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Language Technology (hsv//eng)
Nyckelord
- Private word embedding
- Differential privacy
- UGC
Publikations- och innehållstyp
- ref (ämneskategori)
- kon (ämneskategori)
Hitta via bibliotek
Till lärosätets databas