Abstract
Data privacy protection is an emerging issue in data collection due to increasing concerns related to security and privacy. In the current data collection approaches, data collector is a dominant player who enforces the secure protocol. In other words, privacy protection is only defined by the data collector without the participation of any respondents. Furthermore, the privacy protection becomes more crucial when the raw data analysis is performed by the data collector itself. In view of this, some of the respondents might refuse to contribute their personal data or submit inaccurate data. In this paper, we study a self-awareness protocol to raise the confidence of the respondents when submitting their personal data to the data collector. Our self-awareness protocol requires each respondent to help others in preserving his privacy. At the end of the protocol execution, respondents can verify the protection level (i.e., k-anonymity) they will receive from the data collector.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Sweeney L (2002) k-Anonymity: a model for protecting privacy. Int J Uncertainty Fuzziness Knowl Based Syst 10:557–570
Samarati P, Sweeney L (1998) Generalizing data to provide anonymity when disclosing information (abstract). In: Proceedings of the 7th ACM SIGACT-SIGMOD-SIGART symposium on principles of database systems. ACM, Seattle, Washington, United States, pp 188
Machanavajjhala A, Kifer D, Gehrke J, Venkitasubramaniam M (2007) l-diversity: privacy beyond k-anonymity. ACM Trans Knowl Discov Data 1:3
Fung BCM, Wang K, Chen R, Yu PS (2010) Privacy-preserving data publishing: a survey of recent developments. ACM Comput Surv 42:1–53
Paillier P (1999) Public-key cryptosystems based on composite degree residuosity classes. In: Proceedings of the 17th international conference on theory and application of cryptographic techniques. Springer, Prague, Czech Republic, pp 223–238
Domingo-Ferrer J (2010) Coprivacy: towards a theory of sustainable privacy. In: Proceedings of the 2010 international conference on Privacy in statistical databases. Springer, Corfu, Greece pp 258–268
Domingo-Ferrer J (2011) Coprivacy: an introduction to the theory and applications of co-operative privacy. SORT Stat Oper Res Trans 35:25–40
Golle P, McSherry F, Mironov I (2006) Data collection with self-enforcing privacy. In: Proceedings of the 13th ACM conference on computer and communications security. ACM, Alexandria, Virginia, USA, pp 69–78
Stegelmann M (2010) Towards fair indictment for data collection with self-enforcing privacy. In: Rannenberg K, Varadharajan V, Weber C (eds) Security and privacy—silver linings in the cloud, vol 330. Springer, Berlin Heidelberg, pp 265–276
Kumar R, Gopal RD, Garfinkel RS (2010) Freedom of privacy: anonymous data collection with respondent-defined privacy protection. INFORMS J Comput 22:471–481
Dingledine R, Mathewson N, Syverson P (2004) Tor: the second-generation onion router. In: Proceedings of the 13th conference on USENIX security symposium, vol 13. USENIX Association, San Diego, CA, pp 21–21
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer Science+Business Media Dordrecht
About this paper
Cite this paper
Wong, KS., Kim, M.H. (2014). Privacy-Preserving Data Collection with Self-Awareness Protection. In: Park, J., Zomaya, A., Jeong, HY., Obaidat, M. (eds) Frontier and Innovation in Future Computing and Communications. Lecture Notes in Electrical Engineering, vol 301. Springer, Dordrecht. https://doi.org/10.1007/978-94-017-8798-7_44
Download citation
DOI: https://doi.org/10.1007/978-94-017-8798-7_44
Published:
Publisher Name: Springer, Dordrecht
Print ISBN: 978-94-017-8797-0
Online ISBN: 978-94-017-8798-7
eBook Packages: EngineeringEngineering (R0)