A Privacy Protection Method for Social Network Data against Content/Degree Attacks
Summary :
Recently, social network services are rapidly growing and this trend is expected to continue in the future. Social network data can be published for various purposes such as statistical analysis and population studies. When social network data are published, however, the privacy of some people may be disclosed. The most straightforward manner to preserve privacy in social network data is to remove the identifiers of persons from the social network data. However, an adversary can infer the identity of a person in the social network by using his/her background knowledge, which consists of content information such as the age, sex, or address of the person and structural information such as the number of persons having a relationship with the person. In this paper, we propose a privacy protection method for social network data. The proposed method anonymizes social network data to prevent privacy attacks that use both content and structural information, while minimizing the information loss or distortion of the anonymized social network data. Through extensive experiments, we verify the effectiveness and applicability of the proposed method.
- Publication
- IEICE TRANSACTIONS on Information Vol.E95-D No.1 pp.152-160
- Publication Date
- 2012/01/01
- Publicized
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.E95.D.152
- Type of Manuscript
- Special Section PAPER (Special Section on Trust, Security and Privacy in Computing and Communication Systems)
- Category
- Privacy
Authors
Keyword
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Min Kyoung SUNG, Ki Yong LEE, Jun-Bum SHIN, Yon Dohn CHUNG, "A Privacy Protection Method for Social Network Data against Content/Degree Attacks" in IEICE TRANSACTIONS on Information,
vol. E95-D, no. 1, pp. 152-160, January 2012, doi: 10.1587/transinf.E95.D.152.
Abstract: Recently, social network services are rapidly growing and this trend is expected to continue in the future. Social network data can be published for various purposes such as statistical analysis and population studies. When social network data are published, however, the privacy of some people may be disclosed. The most straightforward manner to preserve privacy in social network data is to remove the identifiers of persons from the social network data. However, an adversary can infer the identity of a person in the social network by using his/her background knowledge, which consists of content information such as the age, sex, or address of the person and structural information such as the number of persons having a relationship with the person. In this paper, we propose a privacy protection method for social network data. The proposed method anonymizes social network data to prevent privacy attacks that use both content and structural information, while minimizing the information loss or distortion of the anonymized social network data. Through extensive experiments, we verify the effectiveness and applicability of the proposed method.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.E95.D.152/_p
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@ARTICLE{e95-d_1_152,
author={Min Kyoung SUNG, Ki Yong LEE, Jun-Bum SHIN, Yon Dohn CHUNG, },
journal={IEICE TRANSACTIONS on Information},
title={A Privacy Protection Method for Social Network Data against Content/Degree Attacks},
year={2012},
volume={E95-D},
number={1},
pages={152-160},
abstract={Recently, social network services are rapidly growing and this trend is expected to continue in the future. Social network data can be published for various purposes such as statistical analysis and population studies. When social network data are published, however, the privacy of some people may be disclosed. The most straightforward manner to preserve privacy in social network data is to remove the identifiers of persons from the social network data. However, an adversary can infer the identity of a person in the social network by using his/her background knowledge, which consists of content information such as the age, sex, or address of the person and structural information such as the number of persons having a relationship with the person. In this paper, we propose a privacy protection method for social network data. The proposed method anonymizes social network data to prevent privacy attacks that use both content and structural information, while minimizing the information loss or distortion of the anonymized social network data. Through extensive experiments, we verify the effectiveness and applicability of the proposed method.},
keywords={},
doi={10.1587/transinf.E95.D.152},
ISSN={1745-1361},
month={January},}
Copy
TY - JOUR
TI - A Privacy Protection Method for Social Network Data against Content/Degree Attacks
T2 - IEICE TRANSACTIONS on Information
SP - 152
EP - 160
AU - Min Kyoung SUNG
AU - Ki Yong LEE
AU - Jun-Bum SHIN
AU - Yon Dohn CHUNG
PY - 2012
DO - 10.1587/transinf.E95.D.152
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E95-D
IS - 1
JA - IEICE TRANSACTIONS on Information
Y1 - January 2012
AB - Recently, social network services are rapidly growing and this trend is expected to continue in the future. Social network data can be published for various purposes such as statistical analysis and population studies. When social network data are published, however, the privacy of some people may be disclosed. The most straightforward manner to preserve privacy in social network data is to remove the identifiers of persons from the social network data. However, an adversary can infer the identity of a person in the social network by using his/her background knowledge, which consists of content information such as the age, sex, or address of the person and structural information such as the number of persons having a relationship with the person. In this paper, we propose a privacy protection method for social network data. The proposed method anonymizes social network data to prevent privacy attacks that use both content and structural information, while minimizing the information loss or distortion of the anonymized social network data. Through extensive experiments, we verify the effectiveness and applicability of the proposed method.
ER -
English
