Zobrazeno 1 - 10
of 23
pro vyhledávání: '"Tianchong Gao"'
Autor:
Tianchong Gao, Feng Li
Publikováno v:
IEEE Access, Vol 8, Pp 185276-185289 (2020)
Online social networks (OSNs) contain sensitive information about individuals, so it's important to anonymize network data before releasing it. Recently, researchers introduced differential privacy to give a strict privacy guarantee. Graph abstractio
Externí odkaz:
https://doaj.org/article/1ef3e3bdffb94b91ad432b08788088fa
Publikováno v:
IEEE Access, Vol 8, Pp 171703-171715 (2020)
The internet is the home for huge volumes of useful data that is constantly being created making it difficult for users to find information relevant to them. Recommendation System is a special type of information filtering system adapted by online ve
Externí odkaz:
https://doaj.org/article/d785089a695d4ee38a6e7b71bdf1bf57
Autor:
Tianchong Gao, Feng Li
Publikováno v:
IEEE Transactions on Network Science and Engineering. 8:3152-3166
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b400528a153ddcb8050333a27505526f
https://doi.org/10.1109/tnse.2021.3106673
https://doi.org/10.1109/tnse.2021.3106673
Autor:
Tianchong Gao, Feng Li
Publikováno v:
2022 IEEE 19th International Conference on Mobile Ad Hoc and Smart Systems (MASS).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::4ffcf186e5f3c09600f09d586ebe292b
https://doi.org/10.1109/mass56207.2022.00076
https://doi.org/10.1109/mass56207.2022.00076
Autor:
Tianchong Gao, Feng Li
Publikováno v:
Proceedings of the 2022 ACM on Asia Conference on Computer and Communications Security.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::22e5566b94ccb9f9444119feb9272d52
https://doi.org/10.1145/3488932.3517405
https://doi.org/10.1145/3488932.3517405
Publikováno v:
IEEE Access, Vol 8, Pp 171703-171715 (2020)
The internet is the home for huge volumes of useful data that is constantly being created making it difficult for users to find information relevant to them. Recommendation System is a special type of information filtering system adapted by online ve
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7ba7554daa84cb38482fbbe4fcb4e7b9
https://doi.org/10.1109/access.2020.3022962
https://doi.org/10.1109/access.2020.3022962
Dissertation/ Thesis
Autor:
Tianchong Gao (7428566)
Following the trend of online data sharing and publishing, researchers raise their concerns about the privacy problem. Online Social Networks (OSNs), for example, often contain sensitive information about individuals. Therefore, anonymizing network d
Publikováno v:
MASS
Collaborative Filtering (CF) is a popular recommendation system that makes recommendations based on similar users’ preferences. Though it is widely used, CF is prone to Shilling/Profile Injection attacks, where fake profiles are injected into the C
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::228ae3c17f7bf5b35e17950e7c96d6a9
https://doi.org/10.1109/mass50613.2020.00050
https://doi.org/10.1109/mass50613.2020.00050
Publikováno v:
GLOBECOM
Online reviews have become an increasingly important factor in the purchase decision of a customer. However, many spammers write deceptive reviews to alter the credibility of a product/service. Often than not, these spammers exhibit group behavior, w
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::eb90ad6a2356ae2a63738fb4b3611ad0
https://doi.org/10.1109/globecom42002.2020.9322330
https://doi.org/10.1109/globecom42002.2020.9322330
Publikováno v:
IEEE Transactions on Computational Social Systems. 5:1009-1020
Following the trend of online social networks (OSNs) data sharing and publishing, users raise serious concerns on OSN privacy. Differential privacy is a mechanism to anonymize sensitive data. It employs graph abstraction models, such as the hierarchi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::4712e2293f2869c2011fda2d5ef7f667
https://doi.org/10.1109/tcss.2018.2877045
https://doi.org/10.1109/tcss.2018.2877045