Privometer: Privacy protection in social networks

Nilothpal Talukder, Mourad Ouzzani, Ahmed Elmagarmid, Hazem Elmeleegy, Mohamed Yakout

Research output: Chapter in Book/Report/Conference proceedingConference contribution

40 Citations (Scopus)

Abstract

The increasing popularity of social networks, such as Facebook and Orkut, has raised several privacy concerns. Traditional ways of safeguarding privacy of personal information by hiding sensitive attributes are no longer adequate. Research shows that probabilistic classification techniques can effectively infer such private information. The disclosed sensitive information of friends, group affiliations and even participation in activities, such as tagging and commenting, are considered background knowledge in this process. In this paper, we present a privacy protection tool, called Privometer, that measures the amount of sensitive information leakage in a user profile and suggests self-sanitization actions to regulate the amount of leakage. In contrast to previous research, where inference techniques use publicly available profile information, we consider an augmented model where a potentially malicious application installed in the user's friend profiles can access substantially more information. In our model, merely hiding the sensitive information is not sufficient to protect the user privacy. We present an implementation of Privometer in Facebook.

Original languageEnglish
Title of host publicationProceedings - International Conference on Data Engineering
Pages266-269
Number of pages4
DOIs
Publication statusPublished - 28 May 2010
Externally publishedYes
Event2010 IEEE 26th International Conference on Data Engineering Workshops, ICDEW 2010 - Long Beach, CA, United States
Duration: 1 Mar 20106 Mar 2010

Other

Other2010 IEEE 26th International Conference on Data Engineering Workshops, ICDEW 2010
CountryUnited States
CityLong Beach, CA
Period1/3/106/3/10

ASJC Scopus subject areas

  • Information Systems
  • Signal Processing
  • Software

Cite this

Talukder, N., Ouzzani, M., Elmagarmid, A., Elmeleegy, H., & Yakout, M. (2010). Privometer: Privacy protection in social networks. In Proceedings - International Conference on Data Engineering (pp. 266-269). [5452715] https://doi.org/10.1109/ICDEW.2010.5452715

Privometer : Privacy protection in social networks. / Talukder, Nilothpal; Ouzzani, Mourad; Elmagarmid, Ahmed; Elmeleegy, Hazem; Yakout, Mohamed.

Proceedings - International Conference on Data Engineering. 2010. p. 266-269 5452715.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Talukder, N, Ouzzani, M, Elmagarmid, A, Elmeleegy, H & Yakout, M 2010, Privometer: Privacy protection in social networks. in Proceedings - International Conference on Data Engineering., 5452715, pp. 266-269, 2010 IEEE 26th International Conference on Data Engineering Workshops, ICDEW 2010, Long Beach, CA, United States, 1/3/10. https://doi.org/10.1109/ICDEW.2010.5452715
Talukder N, Ouzzani M, Elmagarmid A, Elmeleegy H, Yakout M. Privometer: Privacy protection in social networks. In Proceedings - International Conference on Data Engineering. 2010. p. 266-269. 5452715 https://doi.org/10.1109/ICDEW.2010.5452715
Talukder, Nilothpal ; Ouzzani, Mourad ; Elmagarmid, Ahmed ; Elmeleegy, Hazem ; Yakout, Mohamed. / Privometer : Privacy protection in social networks. Proceedings - International Conference on Data Engineering. 2010. pp. 266-269
@inproceedings{dbb0aa583c154020aa909fb0bfb9685d,
title = "Privometer: Privacy protection in social networks",
abstract = "The increasing popularity of social networks, such as Facebook and Orkut, has raised several privacy concerns. Traditional ways of safeguarding privacy of personal information by hiding sensitive attributes are no longer adequate. Research shows that probabilistic classification techniques can effectively infer such private information. The disclosed sensitive information of friends, group affiliations and even participation in activities, such as tagging and commenting, are considered background knowledge in this process. In this paper, we present a privacy protection tool, called Privometer, that measures the amount of sensitive information leakage in a user profile and suggests self-sanitization actions to regulate the amount of leakage. In contrast to previous research, where inference techniques use publicly available profile information, we consider an augmented model where a potentially malicious application installed in the user's friend profiles can access substantially more information. In our model, merely hiding the sensitive information is not sufficient to protect the user privacy. We present an implementation of Privometer in Facebook.",
author = "Nilothpal Talukder and Mourad Ouzzani and Ahmed Elmagarmid and Hazem Elmeleegy and Mohamed Yakout",
year = "2010",
month = "5",
day = "28",
doi = "10.1109/ICDEW.2010.5452715",
language = "English",
isbn = "9781424465217",
pages = "266--269",
booktitle = "Proceedings - International Conference on Data Engineering",

}

TY - GEN

T1 - Privometer

T2 - Privacy protection in social networks

AU - Talukder, Nilothpal

AU - Ouzzani, Mourad

AU - Elmagarmid, Ahmed

AU - Elmeleegy, Hazem

AU - Yakout, Mohamed

PY - 2010/5/28

Y1 - 2010/5/28

N2 - The increasing popularity of social networks, such as Facebook and Orkut, has raised several privacy concerns. Traditional ways of safeguarding privacy of personal information by hiding sensitive attributes are no longer adequate. Research shows that probabilistic classification techniques can effectively infer such private information. The disclosed sensitive information of friends, group affiliations and even participation in activities, such as tagging and commenting, are considered background knowledge in this process. In this paper, we present a privacy protection tool, called Privometer, that measures the amount of sensitive information leakage in a user profile and suggests self-sanitization actions to regulate the amount of leakage. In contrast to previous research, where inference techniques use publicly available profile information, we consider an augmented model where a potentially malicious application installed in the user's friend profiles can access substantially more information. In our model, merely hiding the sensitive information is not sufficient to protect the user privacy. We present an implementation of Privometer in Facebook.

AB - The increasing popularity of social networks, such as Facebook and Orkut, has raised several privacy concerns. Traditional ways of safeguarding privacy of personal information by hiding sensitive attributes are no longer adequate. Research shows that probabilistic classification techniques can effectively infer such private information. The disclosed sensitive information of friends, group affiliations and even participation in activities, such as tagging and commenting, are considered background knowledge in this process. In this paper, we present a privacy protection tool, called Privometer, that measures the amount of sensitive information leakage in a user profile and suggests self-sanitization actions to regulate the amount of leakage. In contrast to previous research, where inference techniques use publicly available profile information, we consider an augmented model where a potentially malicious application installed in the user's friend profiles can access substantially more information. In our model, merely hiding the sensitive information is not sufficient to protect the user privacy. We present an implementation of Privometer in Facebook.

UR - http://www.scopus.com/inward/record.url?scp=77952665888&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=77952665888&partnerID=8YFLogxK

U2 - 10.1109/ICDEW.2010.5452715

DO - 10.1109/ICDEW.2010.5452715

M3 - Conference contribution

AN - SCOPUS:77952665888

SN - 9781424465217

SP - 266

EP - 269

BT - Proceedings - International Conference on Data Engineering

ER -