A Flexible Personal Data Disclosure Method Based on Anonymity Quantification

Miyuki IMADA; Masakatsu OHTA; Mitsuo TERAMOTO; Masayasu YAMAGUCHI

doi:10.1093/ietcom/e90-b.12.3460

A Flexible Personal Data Disclosure Method Based on Anonymity Quantification

Miyuki IMADA, Masakatsu OHTA, Mitsuo TERAMOTO, Masayasu YAMAGUCHI

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In this paper, we propose a method of controlling personal data disclosure based on LooM (Loosely Managed Privacy Protection Method) that prevents a malicious third party from identifying a person when he/she gets context-aware services using personal data. The basic function of LooM quantitatively evaluates the anonymity level of a person who discloses his/her data, and controls the personal-data disclosure according to the level. LooM uses a normalized entropy value for quantifying the anonymity. In this version of the LooM, the disclosure control is accomplished by adding two new functions. One is an abstracting-function that generates abstractions (or summaries) from the raw personal data to reduce the danger that the malicious third party might identify the person who discloses his/her personal data to the party. The other function is a unique-value-masking function that hides the unique personal data in the database. These functions enhance the disclosure control mechanism of LooM. We evaluate the functions using simulation data and questionnaire data. Then, we confirm the effectiveness of the functions. Finally, we show a prototype of a crime-information-sharing service to confirm the feasibility of these functions.

Publication: IEICE TRANSACTIONS on Communications Vol.E90-B No.12 pp.3460-3469

Publication Date: 2007/12/01

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Online ISSN: 1745-1345

DOI: 10.1093/ietcom/e90-b.12.3460

Type of Manuscript: Special Section PAPER (Special Section on Ubiquitous Sensor Networks)

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Miyuki IMADA, Masakatsu OHTA, Mitsuo TERAMOTO, Masayasu YAMAGUCHI, "A Flexible Personal Data Disclosure Method Based on Anonymity Quantification" in IEICE TRANSACTIONS on Communications, vol. E90-B, no. 12, pp. 3460-3469, December 2007, doi: 10.1093/ietcom/e90-b.12.3460.
Abstract: In this paper, we propose a method of controlling personal data disclosure based on LooM (Loosely Managed Privacy Protection Method) that prevents a malicious third party from identifying a person when he/she gets context-aware services using personal data. The basic function of LooM quantitatively evaluates the anonymity level of a person who discloses his/her data, and controls the personal-data disclosure according to the level. LooM uses a normalized entropy value for quantifying the anonymity. In this version of the LooM, the disclosure control is accomplished by adding two new functions. One is an abstracting-function that generates abstractions (or summaries) from the raw personal data to reduce the danger that the malicious third party might identify the person who discloses his/her personal data to the party. The other function is a unique-value-masking function that hides the unique personal data in the database. These functions enhance the disclosure control mechanism of LooM. We evaluate the functions using simulation data and questionnaire data. Then, we confirm the effectiveness of the functions. Finally, we show a prototype of a crime-information-sharing service to confirm the feasibility of these functions.
URL: https://global.ieice.org/en_transactions/communications/10.1093/ietcom/e90-b.12.3460/_p

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@ARTICLE{e90-b_12_3460,
author={Miyuki IMADA, Masakatsu OHTA, Mitsuo TERAMOTO, Masayasu YAMAGUCHI, },
journal={IEICE TRANSACTIONS on Communications},
title={A Flexible Personal Data Disclosure Method Based on Anonymity Quantification},
year={2007},
volume={E90-B},
number={12},
pages={3460-3469},
abstract={In this paper, we propose a method of controlling personal data disclosure based on LooM (Loosely Managed Privacy Protection Method) that prevents a malicious third party from identifying a person when he/she gets context-aware services using personal data. The basic function of LooM quantitatively evaluates the anonymity level of a person who discloses his/her data, and controls the personal-data disclosure according to the level. LooM uses a normalized entropy value for quantifying the anonymity. In this version of the LooM, the disclosure control is accomplished by adding two new functions. One is an abstracting-function that generates abstractions (or summaries) from the raw personal data to reduce the danger that the malicious third party might identify the person who discloses his/her personal data to the party. The other function is a unique-value-masking function that hides the unique personal data in the database. These functions enhance the disclosure control mechanism of LooM. We evaluate the functions using simulation data and questionnaire data. Then, we confirm the effectiveness of the functions. Finally, we show a prototype of a crime-information-sharing service to confirm the feasibility of these functions.},
keywords={},
doi={10.1093/ietcom/e90-b.12.3460},
ISSN={1745-1345},
month={December},}

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TY - JOUR
TI - A Flexible Personal Data Disclosure Method Based on Anonymity Quantification
T2 - IEICE TRANSACTIONS on Communications
SP - 3460
EP - 3469
AU - Miyuki IMADA
AU - Masakatsu OHTA
AU - Mitsuo TERAMOTO
AU - Masayasu YAMAGUCHI
PY - 2007
DO - 10.1093/ietcom/e90-b.12.3460
JO - IEICE TRANSACTIONS on Communications
SN - 1745-1345
VL - E90-B
IS - 12
JA - IEICE TRANSACTIONS on Communications
Y1 - December 2007
AB - In this paper, we propose a method of controlling personal data disclosure based on LooM (Loosely Managed Privacy Protection Method) that prevents a malicious third party from identifying a person when he/she gets context-aware services using personal data. The basic function of LooM quantitatively evaluates the anonymity level of a person who discloses his/her data, and controls the personal-data disclosure according to the level. LooM uses a normalized entropy value for quantifying the anonymity. In this version of the LooM, the disclosure control is accomplished by adding two new functions. One is an abstracting-function that generates abstractions (or summaries) from the raw personal data to reduce the danger that the malicious third party might identify the person who discloses his/her personal data to the party. The other function is a unique-value-masking function that hides the unique personal data in the database. These functions enhance the disclosure control mechanism of LooM. We evaluate the functions using simulation data and questionnaire data. Then, we confirm the effectiveness of the functions. Finally, we show a prototype of a crime-information-sharing service to confirm the feasibility of these functions.
ER -