Zobrazeno 1 - 10
of 116
pro vyhledávání: '"Bolin Ding"'
Publikováno v:
The Journal of Privacy and Confidentiality, Vol 11, Iss 3 (2021)
This paper describes PrivBayes, a differentially private method for generating synthetic datasets that was used in the 2018 Differential Privacy Synthetic Data Challenge organized by NIST.
Externí odkaz:
https://doaj.org/article/77b6c76a597f433795ac5a2eb6140be8
Publikováno v:
Proceedings of the ACM Web Conference 2023.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b37342e36ef7cbe4271d6301219f6bad
https://doi.org/10.1145/3543507.3583280
https://doi.org/10.1145/3543507.3583280
Publikováno v:
ACM Transactions on Information Systems. 40:1-28
Recently, Product Question Answering (PQA) on E-Commerce platforms has attracted increasing attention as it can act as an intelligent online shopping assistant and improve the customer shopping experience. Its key function, automatic answer generatio
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::e56a30bfb58d58d05477cd0b8083ea05
https://doi.org/10.1145/3507782
https://doi.org/10.1145/3507782
Publikováno v:
IEEE Transactions on Knowledge and Data Engineering. :1-13
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b667c48f0f4fd575514b65a66ad70682
https://doi.org/10.1109/tkde.2022.3181270
https://doi.org/10.1109/tkde.2022.3181270
Publikováno v:
IEEE Transactions on Knowledge and Data Engineering. :1-14
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d2a359aa3cd75af0a155e628639fa5ad
https://doi.org/10.1109/tkde.2022.3225109
https://doi.org/10.1109/tkde.2022.3225109
A recent line of works apply machine learning techniques to assist or rebuild cost-based query optimizers in DBMS. While exhibiting superiority in some benchmarks, their deficiencies, e.g., unstable performance, high training cost, and slow model upd
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::488322277dc077aace4640676cd68c39
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783031247545
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::2574dfe8a6b5563b86843223db674493
https://doi.org/10.1007/978-3-031-24755-2_2
https://doi.org/10.1007/978-3-031-24755-2_2
Publikováno v:
Proceedings of the 31st ACM International Conference on Information & Knowledge Management.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::4bd8e544326e39b08fc98488b56eb7f2
https://doi.org/10.1145/3511808.3557395
https://doi.org/10.1145/3511808.3557395
Publikováno v:
Proceedings of the VLDB Endowment. 14:2859-2862
Policy-aware differential privacy (DP) frameworks such as Blowfish privacy enable more accurate query answers than standard DP. In this work, we build the first policy-aware DP system for interactive data exploration, BlowfishDB, that aims to (i) pro
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::e9c59ba67763febe1fe1152e6afbf74b
https://doi.org/10.14778/3476311.3476363
https://doi.org/10.14778/3476311.3476363
Publikováno v:
Proceedings of the VLDB Endowment. 14:2258-2270
Local differential privacy (LDP) is a well-established privacy protection scheme for collecting sensitive data, which has been integrated into major platforms such as iOS, Chrome, and Windows. The main idea is that each individual randomly perturbs h
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::e2b7603e5f52b3909575d96bcde30c08
https://doi.org/10.14778/3476249.3476277
https://doi.org/10.14778/3476249.3476277