STOC '87 Proceedings of the nineteenth annual ACM symposium on Theory of computing
k-anonymity: a model for protecting privacy
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Privacy preserving mining of association rules
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Anonymity-preserving data collection
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Efficient anonymity-preserving data collection
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Optimal Adapter Creation for Process Composition in Synchronous vs. Asynchronous Communication
ACM Transactions on Management Information Systems (TMIS)
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The massive amount of sensitive survey data about individuals that agencies collect and share through the Internet is causing a great deal of privacy concerns. These concerns may discourage individuals from revealing their sensitive information. Existing data collection techniques have serious downsides in terms of both efficiency and the levels of protection they offer against various realizations of threats. Moreover, they do not provide any flexibility to the users to be able to specify acceptable levels of privacy protection before deciding whether to participate in the surveys. In this paper, we propose a two-pronged privacy protection model corresponding to these two privacy concerns: these are a new efficient anonymity preserving data collection technique and a method to incorporate heterogeneous privacy constraints. Together, they help preserve the privacy of respondents both during and after data collection.