TY - GEN
T1 - Privacy-Preserving Collaborative Data Anonymization with Sensitive Quasi-Identifiers
AU - Wong, Kok Seng
AU - Tu, Nguyen Anh
AU - Bui, Dinh Mao
AU - Ooi, Shih Yin
AU - Kim, Myung Ho
N1 - Publisher Copyright:
© 2019 IEEE.
Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2019/11
Y1 - 2019/11
N2 - Collaborative anonymization deals with a group of respondents in a distributed environment. Unlike in centralized settings, no respondent is willing to reveal his or her records to any party due to the privacy concerns. This creates a challenge for anonymization, and it requires a level of trust among respondents. In this paper, we study a collaborative anonymization protocol that aims to increase the confidence of respondents during data collection. Unlike in existing works, our protocol does not reveal the complete set of quasi-identifier (QID) to the data collector (e.g., agency) before and after the data anonymization process. Because QID can be both sensitive values and identifying values, we allow the respondents to hide sensitive-QID attributes from other parties. Our protocol ensures that the desired protection level (i.e., k-anonymity) can be verified before the respondents submit their records to the agency. Furthermore, we allow honest respondents to indict a malicious agency if it modifies the intermediate results or not following the protocol faithfully.
AB - Collaborative anonymization deals with a group of respondents in a distributed environment. Unlike in centralized settings, no respondent is willing to reveal his or her records to any party due to the privacy concerns. This creates a challenge for anonymization, and it requires a level of trust among respondents. In this paper, we study a collaborative anonymization protocol that aims to increase the confidence of respondents during data collection. Unlike in existing works, our protocol does not reveal the complete set of quasi-identifier (QID) to the data collector (e.g., agency) before and after the data anonymization process. Because QID can be both sensitive values and identifying values, we allow the respondents to hide sensitive-QID attributes from other parties. Our protocol ensures that the desired protection level (i.e., k-anonymity) can be verified before the respondents submit their records to the agency. Furthermore, we allow honest respondents to indict a malicious agency if it modifies the intermediate results or not following the protocol faithfully.
KW - Collaborative Anonymization
KW - Data Anonymiza-tion
KW - Data Privacy
KW - k-anonymization
KW - Sensitive Quasi-Identifier
UR - http://www.scopus.com/inward/record.url?scp=85079068025&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85079068025&partnerID=8YFLogxK
U2 - 10.1109/CMI48017.2019.8962140
DO - 10.1109/CMI48017.2019.8962140
M3 - Conference contribution
AN - SCOPUS:85079068025
T3 - 2019 12th CMI Conference on Cybersecurity and Privacy, CMI 2019
BT - 2019 12th CMI Conference on Cybersecurity and Privacy, CMI 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 12th CMI Conference on Cybersecurity and Privacy, CMI 2019
Y2 - 28 November 2019 through 29 November 2019
ER -