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Privacy-Enhancing Queries in Personalized Search with Untrusted Service Providers
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Summary :
For personalized search, a user must provide her personal information. However, this sometimes includes the user's sensitive information about individuals such as health condition and private lifestyle. It is not sufficient just to protect the communication channel between user and service provider. Unfortunately, the collected personal data can potentially be misused for the service providers' commercial advantage (e.g. for advertising methods to target potential consumers). Our aim here is to protect user privacy by filtering out the sensitive information exposed from a user's query input at the system level. We propose a framework by introducing the concept of query generalizer. Query generalizer is a middleware that takes a query for personalized search, modifies the query to hide user's sensitive personal information adaptively depending on the user's privacy policy, and then forwards the modified query to the service provider. Our experimental results show that the best-performing query generalization method is capable of achieving a low traffic overhead within a reasonable range of user privacy. The increased traffic overhead varied from 1.0 to 3.3 times compared to the original query.
- Publication
- IEICE TRANSACTIONS on Information Vol.E95-D No.1 pp.143-151
- Publication Date
- 2012/01/01
- Publicized
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.E95.D.143
- Type of Manuscript
- Special Section PAPER (Special Section on Trust, Security and Privacy in Computing and Communication Systems)
- Category
- Privacy
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Keyword
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Yunsang OH, Hyoungshick KIM, Takashi OBI, "Privacy-Enhancing Queries in Personalized Search with Untrusted Service Providers" in IEICE TRANSACTIONS on Information,
vol. E95-D, no. 1, pp. 143-151, January 2012, doi: 10.1587/transinf.E95.D.143.
Abstract: For personalized search, a user must provide her personal information. However, this sometimes includes the user's sensitive information about individuals such as health condition and private lifestyle. It is not sufficient just to protect the communication channel between user and service provider. Unfortunately, the collected personal data can potentially be misused for the service providers' commercial advantage (e.g. for advertising methods to target potential consumers). Our aim here is to protect user privacy by filtering out the sensitive information exposed from a user's query input at the system level. We propose a framework by introducing the concept of query generalizer. Query generalizer is a middleware that takes a query for personalized search, modifies the query to hide user's sensitive personal information adaptively depending on the user's privacy policy, and then forwards the modified query to the service provider. Our experimental results show that the best-performing query generalization method is capable of achieving a low traffic overhead within a reasonable range of user privacy. The increased traffic overhead varied from 1.0 to 3.3 times compared to the original query.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.E95.D.143/_p
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@ARTICLE{e95-d_1_143,
author={Yunsang OH, Hyoungshick KIM, Takashi OBI, },
journal={IEICE TRANSACTIONS on Information},
title={Privacy-Enhancing Queries in Personalized Search with Untrusted Service Providers},
year={2012},
volume={E95-D},
number={1},
pages={143-151},
abstract={For personalized search, a user must provide her personal information. However, this sometimes includes the user's sensitive information about individuals such as health condition and private lifestyle. It is not sufficient just to protect the communication channel between user and service provider. Unfortunately, the collected personal data can potentially be misused for the service providers' commercial advantage (e.g. for advertising methods to target potential consumers). Our aim here is to protect user privacy by filtering out the sensitive information exposed from a user's query input at the system level. We propose a framework by introducing the concept of query generalizer. Query generalizer is a middleware that takes a query for personalized search, modifies the query to hide user's sensitive personal information adaptively depending on the user's privacy policy, and then forwards the modified query to the service provider. Our experimental results show that the best-performing query generalization method is capable of achieving a low traffic overhead within a reasonable range of user privacy. The increased traffic overhead varied from 1.0 to 3.3 times compared to the original query.},
keywords={},
doi={10.1587/transinf.E95.D.143},
ISSN={1745-1361},
month={January},}
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TY - JOUR
TI - Privacy-Enhancing Queries in Personalized Search with Untrusted Service Providers
T2 - IEICE TRANSACTIONS on Information
SP - 143
EP - 151
AU - Yunsang OH
AU - Hyoungshick KIM
AU - Takashi OBI
PY - 2012
DO - 10.1587/transinf.E95.D.143
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E95-D
IS - 1
JA - IEICE TRANSACTIONS on Information
Y1 - January 2012
AB - For personalized search, a user must provide her personal information. However, this sometimes includes the user's sensitive information about individuals such as health condition and private lifestyle. It is not sufficient just to protect the communication channel between user and service provider. Unfortunately, the collected personal data can potentially be misused for the service providers' commercial advantage (e.g. for advertising methods to target potential consumers). Our aim here is to protect user privacy by filtering out the sensitive information exposed from a user's query input at the system level. We propose a framework by introducing the concept of query generalizer. Query generalizer is a middleware that takes a query for personalized search, modifies the query to hide user's sensitive personal information adaptively depending on the user's privacy policy, and then forwards the modified query to the service provider. Our experimental results show that the best-performing query generalization method is capable of achieving a low traffic overhead within a reasonable range of user privacy. The increased traffic overhead varied from 1.0 to 3.3 times compared to the original query.
ER -
English
