k-anonymity: a model for protecting privacy
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Practical privacy: the SuLQ framework
Proceedings of the twenty-fourth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
L-diversity: Privacy beyond k-anonymity
ACM Transactions on Knowledge Discovery from Data (TKDD)
Output perturbation with query relaxation
Proceedings of the VLDB Endowment
Releasing search queries and clicks privately
Proceedings of the 18th international conference on World wide web
Privacy: Theory meets Practice on the Map
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
A Practical Differentially Private Random Decision Tree Classifier
ICDMW '09 Proceedings of the 2009 IEEE International Conference on Data Mining Workshops
Privacy-preserving data publishing: A survey of recent developments
ACM Computing Surveys (CSUR)
Differential privacy: a survey of results
TAMC'08 Proceedings of the 5th international conference on Theory and applications of models of computation
Data mining with differential privacy
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Boosting the accuracy of differentially private histograms through consistency
Proceedings of the VLDB Endowment
iReduct: differential privacy with reduced relative errors
Proceedings of the 2011 ACM SIGMOD International Conference on Management of data
ICALP'06 Proceedings of the 33rd international conference on Automata, Languages and Programming - Volume Part II
Calibrating noise to sensitivity in private data analysis
TCC'06 Proceedings of the Third conference on Theory of Cryptography
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In recent years, differential privacy data publishing has received considerable attention. However, existing techniques on achieving differential privacy for answering range-count queries fail to release data with high quality. In this paper, we propose a new solution for answering range-count queries under the framework of ε-differential privacy, which aims to maintain high data utility while protecting individual privacy. The key idea of the proposed solution is to add noise on an average tree, in which each node value is the average value of all its leaf nodes. Experimental analysis is designed by comparing the proposed solution and the classic algorithms on the released data utility. The theoretical analysis and experimental results show that our solution is effective and feasible.