Protecting Respondents' Identities in Microdata Release
IEEE Transactions on Knowledge and Data Engineering
k-anonymity: a model for protecting privacy
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Transforming data to satisfy privacy constraints
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
IEEE Transactions on Knowledge and Data Engineering
Bottom-Up Generalization: A Data Mining Solution to Privacy Protection
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Top-Down Specialization for Information and Privacy Preservation
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
Data Privacy through Optimal k-Anonymization
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
Incognito: efficient full-domain K-anonymity
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
On k-anonymity and the curse of dimensionality
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Coding and Information Theory
Blocking Anonymity Threats Raised by Frequent Itemset Mining
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
\ell -Diversity: Privacy Beyond \kappa -Anonymity
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
Injecting utility into anonymized datasets
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
You are what you say: privacy risks of public mentions
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Anonymity preserving pattern discovery
The VLDB Journal — The International Journal on Very Large Data Bases
Robust De-anonymization of Large Sparse Datasets
SP '08 Proceedings of the 2008 IEEE Symposium on Security and Privacy
Anonymizing transaction databases for publication
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
On the Anonymization of Sparse High-Dimensional Data
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Injecting purpose and trust into data anonymisation
Proceedings of the 18th ACM conference on Information and knowledge management
Extended k-anonymity models against sensitive attribute disclosure
Computer Communications
PKDD'05 Proceedings of the 9th European conference on Principles and Practice of Knowledge Discovery in Databases
Satisfying privacy requirements: one step before anonymization
PAKDD'10 Proceedings of the 14th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part I
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We study the challenges of protecting the privacy of individuals in a large public survey rating data. The survey rating data usually contains both ratings of sensitive and non-sensitive issues. The ratings of sensitive issues involve personal privacy. Although the survey participants do not reveal any of their ratings, their survey records are potentially identifiable by using information from other public sources. None of the existing anonymization principles (e.g. k-anonymity, l-diversity, etc.) can effectively prevent such breaches in large survey rating data sets. In this paper, we tackle the problem by defining a principle called (k, epsilon, l)-anonymity. The principle requires that, for each transaction t in the given survey rating data T, at least (k − 1) other transactions in T must have ratings similar to t, where the similarity is controlled by ε and the standard deviation of sensitive ratings is at least l. We propose a greedy approach to anonymize the survey rating data that scales almost linearly with the input size, and we apply the method to two real-life data sets to demonstrate their efficiency and practical utility. Copyright © 2011 John Wiley & Sons, Ltd.