Full Text:   <2242>

Summary:  <1667>

CLC number: TP309

On-line Access: 2015-09-06

Received: 2014-11-16

Revision Accepted: 2015-06-06

Crosschecked: 2015-08-06

Cited: 2

Clicked: 5516

Citations:  Bibtex RefMan EndNote GB/T7714


Myung Ho Kim


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Frontiers of Information Technology & Electronic Engineering  2015 Vol.16 No.9 P.720-731


Towards a respondent-preferred ki-anonymity model

Author(s):  Kok-Seng Wong, Myung Ho Kim

Affiliation(s):  1School of Computer Science and Engineering, Soongsil University, Seoul 06978, Korea; more

Corresponding email(s):   kmh@ssu.ac.kr

Key Words:  Anonymous data collection, Respondent-preferred privacy protection, k-anonymity

Kok-Seng Wong, Myung Ho Kim. Towards a respondent-preferred ki-anonymity model[J]. Frontiers of Information Technology & Electronic Engineering, 2015, 16(9): 720-731.

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Recently, privacy concerns about data collection have received an increasing amount of attention. In data collection process, a data collector (an agency) assumed that all respondents would be comfortable with submitting their data if the published data was anonymous. We believe that this assumption is not realistic because the increase in privacy concerns causes some respondents to refuse participation or to submit inaccurate data to such agencies. If respondents submit inaccurate data, then the usefulness of the results from analysis of the collected data cannot be guaranteed. Furthermore, we note that the level of anonymity (i.e., k-anonymity) guaranteed by an agency cannot be verified by respondents since they generally do not have access to all of the data that is released. Therefore, we introduce the notion of ki-anonymity, where ki is the level of anonymity preferred by each respondent i. Instead of placing full trust in an agency, our solution increases respondent confidence by allowing each to decide the preferred level of protection. As such, our protocol ensures that respondents achieve their preferred ki-anonymity during data collection and guarantees that the collected records are genuine and useful for data analysis.




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