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REACT: REcommending Access Control decisions To social media users

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REACT: REcommending Access Control decisions To social media users. / Misra, Gaurav; Such, Jose.
ASONAM '17 Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017. New York: ACM, 2017.

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review

Harvard

Misra, G & Such, J 2017, REACT: REcommending Access Control decisions To social media users. in ASONAM '17 Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017. ACM, New York. https://doi.org/10.1145/3110025.3110073

APA

Misra, G., & Such, J. (2017). REACT: REcommending Access Control decisions To social media users. In ASONAM '17 Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017 ACM. https://doi.org/10.1145/3110025.3110073

Vancouver

Misra G, Such J. REACT: REcommending Access Control decisions To social media users. In ASONAM '17 Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017. New York: ACM. 2017 doi: 10.1145/3110025.3110073

Author

Misra, Gaurav ; Such, Jose. / REACT : REcommending Access Control decisions To social media users. ASONAM '17 Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017. New York : ACM, 2017.

Bibtex

@inproceedings{4af6c270c436450593004e5d6cef7e30,
title = "REACT: REcommending Access Control decisions To social media users",
abstract = "The problems that social media users have in appropriately controlling access to their content has been well documented in previous research. A promising method of providing assistance to users is by learning from the access control decisions made by them and making future recommendations.In this paper, we present REACT, a learning mechanism which utilizes information available in the social network in conjunction with information about the content to be shared to provide users with access control recommendations. We demonstrate the highly accurate performance of REACT through a detailed empirical evaluation and also discuss ways of personalizing it for different users in order to improve performance even further.",
author = "Gaurav Misra and Jose Such",
year = "2017",
month = jul,
day = "31",
doi = "10.1145/3110025.3110073",
language = "English",
isbn = "9781450349932",
booktitle = "ASONAM '17 Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017",
publisher = "ACM",

}

RIS

TY - GEN

T1 - REACT

T2 - REcommending Access Control decisions To social media users

AU - Misra, Gaurav

AU - Such, Jose

PY - 2017/7/31

Y1 - 2017/7/31

N2 - The problems that social media users have in appropriately controlling access to their content has been well documented in previous research. A promising method of providing assistance to users is by learning from the access control decisions made by them and making future recommendations.In this paper, we present REACT, a learning mechanism which utilizes information available in the social network in conjunction with information about the content to be shared to provide users with access control recommendations. We demonstrate the highly accurate performance of REACT through a detailed empirical evaluation and also discuss ways of personalizing it for different users in order to improve performance even further.

AB - The problems that social media users have in appropriately controlling access to their content has been well documented in previous research. A promising method of providing assistance to users is by learning from the access control decisions made by them and making future recommendations.In this paper, we present REACT, a learning mechanism which utilizes information available in the social network in conjunction with information about the content to be shared to provide users with access control recommendations. We demonstrate the highly accurate performance of REACT through a detailed empirical evaluation and also discuss ways of personalizing it for different users in order to improve performance even further.

U2 - 10.1145/3110025.3110073

DO - 10.1145/3110025.3110073

M3 - Conference contribution/Paper

SN - 9781450349932

BT - ASONAM '17 Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017

PB - ACM

CY - New York

ER -